451 changed files with 77986 additions and 216057 deletions
--- a/.api-extractor.json
+++ b/.api-extractor.json
@ -1,9 +1,8 @@
 {
  "$schema": "https://developer.microsoft.com/json-schemas/api-extractor/v7/api-extractor.schema.json",
  "mainEntryPointFilePath": "types/lib/src/human.d.ts",
  "bundledPackages": ["@tensorflow/tfjs-core", "@tensorflow/tfjs-converter", "@types/offscreencanvas"],
  "compiler": {
-    "skipLibCheck": false
+    "skipLibCheck": true
  },
  "newlineKind": "lf",
  "dtsRollup": {
@ -11,28 +10,18 @@
    "untrimmedFilePath": "types/human.d.ts"
  },
  "docModel": { "enabled": false },
-  "tsdocMetadata": {
+  "tsdocMetadata": { "enabled": false },
    "enabled": false
  },
  "apiReport": { "enabled": false },
  "messages": {
    "compilerMessageReporting": {
-      "default": {
+      "default": { "logLevel": "warning" }
        "logLevel": "warning"
      }
    },
    "extractorMessageReporting": {
-      "default": {
+      "default": { "logLevel": "warning" },
-        "logLevel": "warning"
+      "ae-missing-release-tag": { "logLevel": "none" }
      },
      "ae-missing-release-tag": {
        "logLevel": "none"
      }
    },
    "tsdocMessageReporting": {
-      "default": {
+      "default": { "logLevel": "warning" }
        "logLevel": "warning"
      }
    }
  }
 }
--- a/.build.json
+++ b/.build.json
@ -8,13 +8,14 @@
  "profiles": {
    "production": ["clean", "compile", "typings", "typedoc", "lint", "changelog"],
    "development": ["serve", "watch", "compile"],
-    "serve": ["serve"]
+    "serve": ["serve"],
    "clean": ["clean"]
  },
  "clean": {
-    "locations": ["dist/*", "types/lib/*", "typedoc/*"]
+    "locations": ["dist/*", "types/*", "typedoc/*"]
  },
  "lint": {
-    "locations": [ "*.json", "src/**/*.ts", "test/**/*.js", "demo/**/*.js" ],
+    "locations": [ "**/*.json", "src/**/*.ts", "test/**/*.js", "demo/**/*.js", "**/*.md" ],
    "rules": { }
  },
  "changelog": {
@ -23,8 +24,8 @@
  "serve": {
    "sslKey": "node_modules/@vladmandic/build/cert/https.key",
    "sslCrt": "node_modules/@vladmandic/build/cert/https.crt",
-    "httpPort": 10030,
+    "httpPort": 8000,
-    "httpsPort": 10031,
+    "httpsPort": 8001,
    "documentRoot": ".",
    "defaultFolder": "demo",
    "defaultFile": "index.html"
@ -38,6 +39,13 @@
      "banner": { "js": "/*\n  Human\n  homepage: <https://github.com/vladmandic/human>\n  author: <https://github.com/vladmandic>'\n*/\n" }
    },
    "targets": [
      { 
        "name": "tfjs/browser/version",
        "platform": "browser",
        "format": "esm",
        "input": "tfjs/tf-version.ts",
        "output": "dist/tfjs.version.js"
      },
      {
        "name": "tfjs/nodejs/cpu",
        "platform": "node",
@ -76,6 +84,7 @@
        "format": "cjs",
        "input": "tfjs/tf-node-wasm.ts",
        "output": "dist/tfjs.esm.js",
        "minify": false,
        "external": ["@tensorflow"]
      },
      {
@ -86,13 +95,6 @@
        "output": "dist/human.node-wasm.js",
        "external": ["@tensorflow"]
      },
      { 
        "name": "tfjs/browser/version",
        "platform": "browser",
        "format": "esm",
        "input": "tfjs/tf-version.ts",
        "output": "dist/tfjs.version.js"
      },
      {
        "name": "tfjs/browser/esm/nobundle",
        "platform": "browser",
@ -107,16 +109,17 @@
        "format": "esm",
        "input": "src/human.ts",
        "output": "dist/human.esm-nobundle.js",
-        "sourcemap": true,
+        "sourcemap": false,
        "external": ["@tensorflow"]
      },
      {
-        "name": "tfjs/browser/esm/custom",
+        "name": "tfjs/browser/esm/bundle",
        "platform": "browser",
        "format": "esm",
-        "input": "tfjs/tf-custom.ts",
+        "input": "tfjs/tf-browser.ts",
        "output": "dist/tfjs.esm.js",
-        "sourcemap": false
+        "sourcemap": false,
        "minify": true
      },
      {
        "name": "human/browser/iife/bundle",
@ -157,6 +160,15 @@
        "output": "demo/faceid/index.js",
        "sourcemap": true,
        "external": ["*/human.esm.js"]
      },
      {
        "name": "demo/tracker",
        "platform": "browser",
        "format": "esm",
        "input": "demo/tracker/index.ts",
        "output": "demo/tracker/index.js",
        "sourcemap": true,
        "external": ["*/human.esm.js"]
      }
    ]
  },
--- a/.eslintrc.json
+++ b/.eslintrc.json
@ -1,93 +1,221 @@
 {
-  "globals": {},
+  "globals": {
  },
  "rules": {
    "@typescript-eslint/no-require-imports":"off"
  },
  "overrides": [
    {
      "files": ["**/*.ts"],
      "parser": "@typescript-eslint/parser",
      "parserOptions": { "ecmaVersion": "latest", "project": ["./tsconfig.json"] },
      "plugins": ["@typescript-eslint"],
      "env": {
        "browser": true,
        "commonjs": false,
        "node": false,
        "es2021": true
      },
      "extends": [
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        "plugin:@typescript-eslint/eslint-recommended",
        "plugin:@typescript-eslint/recommended",
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        "plugin:@typescript-eslint/strict",
        "plugin:import/recommended",
        "plugin:promise/recommended"
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      "rules": {
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      "files": ["**/*.d.ts"],
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      "env": {
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  "parser": "@typescript-eslint/parser",
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  "plugins": [
    "@typescript-eslint",
    "html"
  ],
      "extends": [
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    "plugin:@typescript-eslint/eslint-recommended",
    "plugin:@typescript-eslint/recommended",
    "plugin:import/errors",
    "plugin:import/warnings",
    "plugin:json/recommended-with-comments",
        "plugin:node/recommended",
        "plugin:promise/recommended"
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  "ignorePatterns": [
    "assets",
    "demo/helpers/*.js",
    "demo/typescript/*.js",
    "demo/faceid/*.js",
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    "media",
    "models",
    "node_modules",
    "types/human.d.ts"
  ],
      "rules": {
    "@typescript-eslint/ban-ts-comment": "off",
    "@typescript-eslint/explicit-module-boundary-types": "off",
    "@typescript-eslint/no-shadow": "error",
    "@typescript-eslint/no-var-requires": "off",
    "@typescript-eslint/prefer-as-const": "off",
    "@typescript-eslint/triple-slash-reference": "off",
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    "@typescript-eslint/no-empty-interface": ["error", { "allowSingleExtends": true }],
    "camelcase": "off",
    "class-methods-use-this": "off",
        "dot-notation":"off",
-    "func-names": "off",
+        "import/extensions": ["error", "always"],
    "guard-for-in": "off",
    "import/extensions": "off",
    "import/named": "off",
        "import/no-extraneous-dependencies":"off",
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      "files": ["**/*.html"],
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      "parser": "@html-eslint/parser",
      "plugins": ["html", "@html-eslint"],
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        "@html-eslint/indent": ["error", 2]
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      "plugins": ["markdown"],
      "processor": "markdown/markdown",
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        "no-undef":"off"
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      "files": ["**/*.md/*.js"],
      "rules": {
        "@typescript-eslint/no-unused-vars":"off",
        "@typescript-eslint/triple-slash-reference":"off",
        "import/newline-after-import":"off",
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        "no-console":"off",
        "no-global-assign":"off",
        "no-multi-spaces":"off",
        "no-restricted-globals":"off",
        "no-undef":"off",
        "no-unused-vars":"off",
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    "demo/typescript/*.js",
    "demo/faceid/*.js",
    "demo/tracker/*.js",
    "typedoc"
  ]
 }
--- a/.github/FUNDING.yml
+++ b/.github/FUNDING.yml
@ -0,0 +1,11 @@
 github: [vladmandic]
 patreon: # Replace with a single Patreon username
 open_collective: # Replace with a single Open Collective username
 ko_fi: # Replace with a single Ko-fi username
 tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
 community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
 liberapay: # Replace with a single Liberapay username
 issuehunt: # Replace with a single IssueHunt username
 otechie: # Replace with a single Otechie username
 lfx_crowdfunding: # Replace with a single LFX Crowdfunding project-name e.g., cloud-foundry
 custom: # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']
--- a/.gitignore
+++ b/.gitignore
@ -1,6 +1,9 @@
-.vscode
+node_modules/
 node_modules
 pnpm-lock.yaml
 assets/tf*
 *.swp
 types/lib
 pnpm-lock.yaml
 package-lock.json
 *.swp
 samples/**/*.mp4
 samples/**/*.webm
 temp
 tmp
--- a/.markdownlint.json
+++ b/.markdownlint.json
@ -1,6 +1,7 @@
 {
  "MD012": false,
  "MD013": false,
  "MD029": false,
  "MD033": false,
  "MD036": false,
  "MD041": false
--- a/.npmrc
+++ b/.npmrc
@ -1 +1,5 @@
 force=true
 omit=dev
 legacy-peer-deps=true
 strict-peer-dependencies=false
 node-options='--no-deprecation'
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@ -0,0 +1,10 @@
 {
  "search.exclude": {
    "dist/*": true,
    "node_modules/*": true,
    "types": true,
    "typedoc": true,
  },
  "search.useGlobalIgnoreFiles": true,
  "search.useParentIgnoreFiles": true
 }
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@ -1,19 +1,259 @@
-#   
+# @vladmandic/human  
-  Version: **undefined**  
+  Version: **3.3.5**  
-  Description: **undefined**  
+  Description: **Human: AI-powered 3D Face Detection & Rotation Tracking, Face Description & Recognition, Body Pose Tracking, 3D Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction, Gesture Recognition**  
-  Author: **undefined**  
+  Author: **Vladimir Mandic <mandic00@live.com>**  
-  License: **undefined**  
+  License: **MIT**  
  Repository: **<https://github.com/vladmandic/human>**  
 ## Changelog
 ### **3.3.5** 2025/02/05 mandic00@live.com
 ### **origin/main** 2024/10/24 mandic00@live.com
 - add human.draw.tensor method
 ### **3.3.4** 2024/10/24 mandic00@live.com
 ### **3.3.3** 2024/10/14 mandic00@live.com
 - add loaded property to model stats and mark models not loaded correctly.
 - release build
 ### **3.3.2** 2024/09/11 mandic00@live.com
 - full rebuild
 ### **3.3.1** 2024/09/11 mandic00@live.com
 - add config.face.detector.square option
 - human 3.3 alpha test run
 - human 3.3 alpha with new build environment
 - release rebuild
 - fix flazeface tensor scale and update build platform
 ### **3.2.2** 2024/04/17 mandic00@live.com
 ### **release: 3.2.1** 2024/02/15 mandic00@live.com
 ### **3.2.1** 2024/02/15 mandic00@live.com
 ### **3.2.0** 2023/12/06 mandic00@live.com
 - set browser false when navigator object is empty
 - https://github.com/vladmandic/human/issues/402
 ### **release: 3.1.2** 2023/09/18 mandic00@live.com
 - full rebuild
 ### **3.1.2** 2023/09/18 mandic00@live.com
 - major toolkit upgrade
 - full rebuild
 - major toolkit upgrade
 ### **3.1.1** 2023/08/05 mandic00@live.com
 - fixes plus tfjs upgrade for new release
 ### **3.0.7** 2023/06/12 mandic00@live.com
 - full rebuild
 - fix memory leak in histogramequalization
 - initial work on tracker
 ### **3.0.6** 2023/03/21 mandic00@live.com
 - add optional crop to multiple models
 - fix movenet-multipose
 - add electron detection
 - fix gender-ssrnet-imdb
 - add movenet-multipose workaround
 - rebuild and publish
 - add face.detector.minsize configurable setting
 - add affectnet
 ### **3.0.5** 2023/02/02 mandic00@live.com
 - add gear-e models
 - detect react-native
 - redo blazeface annotations
 ### **3.0.4** 2023/01/29 mandic00@live.com
 - make naviator calls safe
 - fix facedetector-only configs
 ### **3.0.3** 2023/01/07 mandic00@live.com
 - full rebuild
 ### **3.0.2** 2023/01/06 mandic00@live.com
 - default face.rotation disabled
 ### **release: 3.0.1** 2022/11/22 mandic00@live.com
 ### **3.0.1** 2022/11/22 mandic00@live.com
 - support dynamic loads
 - polish demos
 - add facedetect demo and fix model async load
 - enforce markdown linting
 - cleanup git history
 - default empty result
 - refactor draw and models namespaces
 - refactor distance
 - add basic anthropometry
 - added webcam id specification
 - include external typedefs
 - prepare external typedefs
 - rebuild all
 - include project files for types
 - architectural improvements
 - refresh dependencies
 - add named exports
 - add draw label templates
 - reduce dev dependencies
 - tensor rank strong typechecks
 - rebuild dependencies
 ### **2.11.1** 2022/10/09 mandic00@live.com
 - add rvm segmentation model
 - add human.webcam methods
 - create funding.yml
 - fix rotation interpolation
 ### **2.10.3** 2022/09/21 mandic00@live.com
 - add human.video method
 ### **2.10.2** 2022/09/11 mandic00@live.com
 - add node.js esm compatibility (#292)
 - release
 ### **2.10.1** 2022/09/07 mandic00@live.com
 - release candidate
 - add config flags
 - test update
 - release preview
 - optimize startup sequence
 - reorder backend init code
 - test embedding
 - embedding test
 - add browser iife tests
 - minor bug fixes and increased test coverage
 - extend release tests
 - add model load exception handling
 - add softwarekernels config option
 - expand type safety
 - full eslint rule rewrite
 ### **2.9.4** 2022/08/20 mandic00@live.com
 - add browser test
 - add tensorflow library detection
 - fix wasm detection
 - enumerate additional models
 - release refresh
 ### **2.9.3** 2022/08/10 mandic00@live.com
 - rehault testing framework
 - release refresh
 - add insightface
 ### **2.9.2** 2022/08/08 mandic00@live.com
 - release rebuild
 ### **2.9.1** 2022/07/25 mandic00@live.com
 - full rebuild
 - release cleanup
 - tflite experiments
 - add load monitor test
 - beta for upcoming major release
 - swtich to release version of tfjs
 - placeholder for face contours
 - improve face compare in main demo
 - add webview support
 - fix(gear): ensure gear.modelpath is used for loadmodel()
 - npm default install should be prod only
 - fix npm v7 compatibility
 - add getmodelstats method
 - rebuild
 - release build
 ### **2.8.1** 2022/06/08 mandic00@live.com
 - webgpu and wasm optimizations
 - add faceboxes prototype
 - full rebuild
 ### **2.7.4** 2022/05/24 mandic00@live.com
 ### **2.7.3** 2022/05/24 mandic00@live.com
 - add face.mesh.keepinvalid config flag
 - initial work for new facemesh model
 ### **2.7.2** 2022/05/12 mandic00@live.com
 - fix demo when used with video files
 - major release
 ### **2.7.1** 2022/05/09 mandic00@live.com
 - support 4k input
 - add attention draw methods
 - fix coloring function
 - enable precompile as part of warmup
 - prepare release beta
 - change default face crop
 - beta release 2.7
 - refactor draw methods
 - implement face attention model
 - add electronjs demo
 - rebuild
 ### **2.6.5** 2022/04/01 mandic00@live.com
 - bundle offscreencanvas types
 - prototype precompile pass
 - fix changelog generation
 - fix indexdb config check
 ### **2.6.4** 2022/02/27 mandic00@live.com
 - fix types typo
 - refresh
 - add config option wasmplatformfetch
 ### **2.6.3** 2022/02/10 mandic00@live.com
 - rebuild
 ### **2.6.2** 2022/02/07 mandic00@live.com
 - release rebuild
 ### **2.6.1** 2022/01/20 mandic00@live.com
-
+- implement model caching using indexdb
 ### **origin/main** 2022/01/17 mandic00@live.com
 - prototype global fetch handler
 - fix face box and hand tracking when in front of face
@ -26,9 +266,7 @@
 ### **2.5.7** 2021/12/27 mandic00@live.com
 - fix posenet
-
+- release refresh
 ### **release: 2.5.6** 2021/12/15 mandic00@live.com
 ### **2.5.6** 2021/12/15 mandic00@live.com
@ -77,9 +315,7 @@
 - rebuild
 - add type defs when working with relative path imports
 - disable humangl backend if webgl 1.0 is detected
-
+- add additional hand gestures
 ### **release: 2.5.1** 2021/11/08 mandic00@live.com
 ### **2.5.1** 2021/11/08 mandic00@live.com
@ -107,9 +343,7 @@
 - add ts demo
 - switch from es2018 to es2020 for main build
 - switch to custom tfjs for demos
-
+- release 2.4
 ### **release: 2.4.1** 2021/10/25 mandic00@live.com
 ### **2.4.1** 2021/10/25 mandic00@live.com
@ -136,7 +370,6 @@
 - minor blazepose optimizations
 - compress samples
 - remove handdetect from default package
 - remove posenet from default package
 - enhanced movenet postprocessing
 - use transferrable buffer for worker messages
@ -144,17 +377,13 @@
 - add node-match advanced example using worker thread pool
 - package updates
 - optimize image preprocessing
-
+- set webgpu optimized flags
 ### **release: 2.3.2** 2021/10/11 mandic00@live.com
 - major precision improvements to movenet and handtrack
 - image processing fixes
 - redesign body and hand caching and interpolation
 - demo default config cleanup
 - improve gaze and face angle visualizations in draw
-
+- release 2.3.1
 ### **release 2.3.1** 2021/10/06 mandic00@live.com
 ### **2.3.1** 2021/10/06 mandic00@live.com
@ -164,7 +393,6 @@
 - fix backend order initialization
 - added docker notes
 - breaking change: new similarity and match methods
 - release candidate
 - tweaked default values
 - enable handtrack as default model
 - redesign face processing
@ -194,9 +422,7 @@
 ### **2.2.2** 2021/09/17 mandic00@live.com
 - experimental webgl status monitoring
-
+- major release
 ### **release: 2.2.1** 2021/09/16 mandic00@live.com
 ### **2.2.1** 2021/09/16 mandic00@live.com
@ -225,8 +451,6 @@
 - implement event emitters
 - fix iife loader
 - simplify dependencies
 - fix file permissions
 - remove old build server
 - change build process
 - add benchmark info
 - simplify canvas handling in nodejs
@ -269,7 +493,6 @@
 ### **2.1.1** 2021/07/29 mandic00@live.com
 - proposal #141
 - add note on manually disping tensor
 - modularize model loading
@ -283,9 +506,7 @@
 - reorganize demos
 - fix centernet box width & height
 - add body segmentation sample
-
+- add release notes
 ### **release: 2.0.1** 2021/06/08 mandic00@live.com
 - release 2.0
 ### **2.0.1** 2021/06/08 mandic00@live.com
@ -314,7 +535,6 @@
 - implemented human.next global interpolation method
 - finished draw buffering and smoothing and enabled by default
 - implemented service worker
 - quantized centernet
 - release candidate
 - added usage restrictions
 - quantize handdetect model
@ -348,8 +568,6 @@
 ### **1.9.1** 2021/05/21 mandic00@live.com
 - caching improvements
 - sanitize server input
 - remove nanodet weights from default distribution
 - add experimental mb3-centernet object detection
 - individual model skipframes values still max high threshold for caching
 - config.videooptimized has been removed and config.cachesensitivity has been added instead
@ -371,9 +589,7 @@
 ### **1.8.2** 2021/05/04 mandic00@live.com
-
+- release 1.8 with major changes and tfjs 3.6.0
 ### **release 1.8 with major changes and tfjs 3.6.0** 2021/04/30 mandic00@live.com
 ### **1.8.1** 2021/04/30 mandic00@live.com
@ -407,7 +623,6 @@
 - added filter.flip feature
 - added demo load image from http
 - mobile demo optimization and iris gestures
 - full test run
 - full rebuild
 - new look
 - added benchmarks
@ -517,7 +732,6 @@
 - add experimental nanodet object detection
 - full models signature
 - cleanup
 ### **1.1.7** 2021/03/16 mandic00@live.com
@ -563,7 +777,6 @@
 ### **1.0.3** 2021/03/10 mandic00@live.com
 - strong typing for public classes and hide private classes
 - re-added blazeface-front
 - enhanced age, gender, emotion detection
 - full rebuild
@ -572,9 +785,6 @@
 - remove blazeface-front, blazepose-upper, faceboxes
 - remove blazeface-front and faceboxes
 ### **release: 1.0.1** 2021/03/09 mandic00@live.com
 ### **1.0.1** 2021/03/09 mandic00@live.com
 - fix for face detector when mesh is disabled
@ -589,7 +799,6 @@
 - 0.40.5
 - fix human.draw
 - 0.40.4
 - cleanup blazepose code
 - fix demo
 - 0.40.3
 - 0.40.2
@ -612,19 +821,13 @@
 - 0.20.11
 - 0.20.10
 - 0.20.9
 - remove extra items
 - simmilarity fix
 - 0.20.8
 - embedding fix
 - 0.20.7
 - build fix
 - 0.20.6
 - embedding fix
 - 0.20.5
 - fix imagefx and add dev builds
 ### **0.20.4** 2021/02/19 mandic00@live.com
 - 0.20.4
 - 0.20.3
 - rebuild
@ -649,7 +852,6 @@
 ### **0.9.26** 2021/01/18 mandic00@live.com
 - fix face detection when mesh is disabled
 - added minification notes
 - version bump
 ### **0.9.25** 2021/01/13 mandic00@live.com
@ -711,7 +913,6 @@
 - conditional hand rotation
 - staggered skipframes
 - fix permissions
 ### **0.9.13** 2020/12/08 mandic00@live.com
@ -820,7 +1021,6 @@
 - optimized model loader
 - merge branch 'main' of https://github.com/vladmandic/human into main
 - created wiki
 - delete bug_report.md
 - optimize font resizing
 - fix nms sync call
@ -844,7 +1044,6 @@
 - optimized camera and mobile layout
 - fixed worker and filter compatibility
 - removed test code
 ### **0.7.2** 2020/11/04 mandic00@live.com
@ -921,7 +1120,6 @@
 ### **0.4.8** 2020/10/28 mandic00@live.com
 - revert "updated menu handler"
 - fix webpack compatibility issue
 ### **0.4.7** 2020/10/27 mandic00@live.com
@ -1009,7 +1207,6 @@
 ### **0.2.8** 2020/10/13 mandic00@live.com
 - added example image
 ### **0.2.7** 2020/10/13 mandic00@live.com
@ -1025,7 +1222,6 @@
 ### **0.2.4** 2020/10/12 mandic00@live.com
 - removed extra files
 ### **0.2.3** 2020/10/12 mandic00@live.com
--- a/README.md
+++ b/README.md
@ -1,9 +1,9 @@
 [![](https://img.shields.io/static/v1?label=Sponsor&message=%E2%9D%A4&logo=GitHub&color=%23fe8e86)](https://github.com/sponsors/vladmandic)
 ![Git Version](https://img.shields.io/github/package-json/v/vladmandic/human?style=flat-square&svg=true&label=git)
 ![NPM Version](https://img.shields.io/npm/v/@vladmandic/human.png?style=flat-square)
 ![Last Commit](https://img.shields.io/github/last-commit/vladmandic/human?style=flat-square&svg=true)
 ![License](https://img.shields.io/github/license/vladmandic/human?style=flat-square&svg=true)
 ![GitHub Status Checks](https://img.shields.io/github/checks-status/vladmandic/human/main?style=flat-square&svg=true)
 ![Vulnerabilities](https://img.shields.io/snyk/vulnerabilities/github/vladmandic/human?style=flat-square&svg=true)
 # Human Library
@ -13,26 +13,37 @@
 <br>
-JavaScript module using TensorFlow/JS Machine Learning library  
+## Highlights
- **Browser**:  
+- Compatible with most server-side and client-side environments and frameworks  
-  Compatible with both desktop and mobile platforms  
+- Combines multiple machine learning models which can be switched on-demand depending on the use-case  
-  Compatible with *CPU*, *WebGL*, *WASM* backends  
+- Related models are executed in an attention pipeline to provide details when needed  
-  Compatible with *WebWorker* execution
+- Optimized input pre-processing that can enhance image quality of any type of inputs  
- **NodeJS**:  
+- Detection of frame changes to trigger only required models for improved performance  
-  Compatible with both software *tfjs-node* and  
+- Intelligent temporal interpolation to provide smooth results regardless of processing performance  
-  GPU accelerated backends *tfjs-node-gpu* using CUDA libraries  
+- Simple unified API  
 - Built-in Image, Video and WebCam handling
 [*Jump to Quick Start*](#quick-start)
 <br>
-*Check out [**Simple Live Demo**](https://vladmandic.github.io/human/demo/typescript/index.html) fully annotated app as a good start starting point ([html](https://github.com/vladmandic/human/blob/main/demo/typescript/index.html))([code](https://github.com/vladmandic/human/blob/main/demo/typescript/index.ts))*  
+## Compatibility
-*Check out [**Main Live Demo**](https://vladmandic.github.io/human/demo/index.html) app for advanced processing of of webcam, video stream or images static images with all possible tunable options*  
+**Browser**:  
  - Compatible with both desktop and mobile platforms  
  - Compatible with *WebGPU*, *WebGL*, *WASM*, *CPU* backends  
  - Compatible with *WebWorker* execution  
  - Compatible with *WebView*  
  - Primary platform: *Chromium*-based browsers  
  - Secondary platform: *Firefox*, *Safari*
- To start video detection, simply press *Play*  
+**NodeJS**:  
- To process images, simply drag & drop in your Browser window  
+  - Compatibile with *WASM* backend for executions on architectures where *tensorflow* binaries are not available  
- Note: For optimal performance, select only models you'd like to use
+  - Compatible with *tfjs-node* using software execution via *tensorflow* shared libraries  
- Note: If you have modern GPU, WebGL (default) backend is preferred, otherwise select WASM backend
+  - Compatible with *tfjs-node* using GPU-accelerated execution via *tensorflow* shared libraries and nVidia CUDA  
  - Supported versions are from **14.x** to **22.x**  
  - NodeJS version **23.x** is not supported due to breaking changes and issues with `@tensorflow/tfjs`  
 <br>
@ -41,39 +52,60 @@ JavaScript module using TensorFlow/JS Machine Learning library
 - [NPM Link](https://www.npmjs.com/package/@vladmandic/human)
 ## Demos
 *Check out [**Simple Live Demo**](https://vladmandic.github.io/human/demo/typescript/index.html) fully annotated app as a good start starting point ([html](https://github.com/vladmandic/human/blob/main/demo/typescript/index.html))([code](https://github.com/vladmandic/human/blob/main/demo/typescript/index.ts))*  
 *Check out [**Main Live Demo**](https://vladmandic.github.io/human/demo/index.html) app for advanced processing of of webcam, video stream or images static images with all possible tunable options*  
 - To start video detection, simply press *Play*  
 - To process images, simply drag & drop in your Browser window  
 - Note: For optimal performance, select only models you'd like to use
 - Note: If you have modern GPU, *WebGL* (default) backend is preferred, otherwise select *WASM* backend
 <br>
 - [**List of all Demo applications**](https://github.com/vladmandic/human/wiki/Demos)
 - [**Live Examples galery**](https://vladmandic.github.io/human/samples/index.html)
 ### Browser Demos
 *All browser demos are self-contained without any external dependencies*
 - **Full** [[*Live*]](https://vladmandic.github.io/human/demo/index.html) [[*Details*]](https://github.com/vladmandic/human/tree/main/demo): Main browser demo app that showcases all Human capabilities
 - **Simple** [[*Live*]](https://vladmandic.github.io/human/demo/typescript/index.html) [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/typescript): Simple demo in WebCam processing demo in TypeScript
- **Face Match** [[*Live*]](https://vladmandic.github.io/human/demo/facematch/index.html) [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/facematch): Extract faces from images, calculates face descriptors and simmilarities and matches them to known database
+- **Embedded** [[*Live*]](https://vladmandic.github.io/human/demo/video/index.html) [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/video/index.html): Even simpler demo with tiny code embedded in HTML file
 - **Face Detect** [[*Live*]](https://vladmandic.github.io/human/demo/facedetect/index.html) [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/facedetect): Extract faces from images and processes details
 - **Face Match** [[*Live*]](https://vladmandic.github.io/human/demo/facematch/index.html) [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/facematch): Extract faces from images, calculates face descriptors and similarities and matches them to known database
 - **Face ID** [[*Live*]](https://vladmandic.github.io/human/demo/faceid/index.html) [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/faceid): Runs multiple checks to validate webcam input before performing face match to faces in IndexDB
 - **Multi-thread** [[*Live*]](https://vladmandic.github.io/human/demo/multithread/index.html) [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/multithread): Runs each Human module in a separate web worker for highest possible performance  
 - **NextJS** [[*Live*]](https://vladmandic.github.io/human-next/out/index.html) [[*Details*]](https://github.com/vladmandic/human-next): Use Human with TypeScript, NextJS and ReactJS
- **3D Analysis** [[*Live*]](https://vladmandic.github.io/human-motion/src/index.html) [[*Details*]](https://github.com/vladmandic/human-motion): 3D tracking and visualization of heead, face, eye, body and hand
+- **ElectronJS** [[*Details*]](https://github.com/vladmandic/human-electron): Use Human with TypeScript and ElectonJS to create standalone cross-platform apps
- **Avatar Bone Mapping** [[*Live*]](https://vladmandic.github.io/human-vrm/src/human-avatar.html) [[*Details*]](https://github.com/vladmandic/human-avatar): Human skeleton with full bone mapping using look and inverse kinematics controllers
+- **3D Analysis with BabylonJS** [[*Live*]](https://vladmandic.github.io/human-motion/src/index.html) [[*Details*]](https://github.com/vladmandic/human-motion): 3D tracking and visualization of heead, face, eye, body and hand
- **Virtual Model Tracking** [[*Live*]](https://vladmandic.github.io/human-vrm/src/human-vrm.html) [[*Details*]](https://github.com/vladmandic/human-vrm): VR model with head, face, eye, body and hand tracking  
+- **VRM Virtual Model Tracking with Three.JS** [[*Live*]](https://vladmandic.github.io/human-three-vrm/src/human-vrm.html) [[*Details*]](https://github.com/vladmandic/human-three-vrm): VR model with head, face, eye, body and hand tracking  
 - **VRM Virtual Model Tracking with BabylonJS** [[*Live*]](https://vladmandic.github.io/human-bjs-vrm/src/index.html) [[*Details*]](https://github.com/vladmandic/human-bjs-vrm): VR model with head, face, eye, body and hand tracking  
 ### NodeJS Demos
- **Main** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/nodejs): Process images from files, folders or URLs using native methods  
+*NodeJS demos may require extra dependencies which are used to decode inputs*  
- **Canvas** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/nodejs): Process image from file or URL and draw results to a new image file using `node-canvas`  
+*See header of each demo to see its dependencies as they are not automatically installed with `Human`*
 - **Video** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/nodejs): Processing of video input using `ffmpeg`  
 - **WebCam** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/nodejs): Processing of webcam screenshots using `fswebcam`  
 - **Events** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/nodejs): Showcases usage of `Human` eventing to get notifications on processing
 - **Similarity** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/nodejs): Compares two input images for similarity of detected faces
 - **Face Match** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/facematch): Parallel processing of face **match** in multiple child worker threads
 - **Multiple Workers** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/nodejs): Runs multiple parallel `human` by dispaching them to pool of pre-created worker processes  
 - **Main** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/nodejs/node.js): Process images from files, folders or URLs using native methods  
 - **Canvas** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/nodejs/node-canvas.js): Process image from file or URL and draw results to a new image file using `node-canvas`  
 - **Video** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/nodejs/node-video.js): Processing of video input using `ffmpeg`  
 - **WebCam** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/nodejs/node-webcam.js): Processing of webcam screenshots using `fswebcam`  
 - **Events** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/nodejs/node-event.js): Showcases usage of `Human` eventing to get notifications on processing
 - **Similarity** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/nodejs/node-similarity.js): Compares two input images for similarity of detected faces
 - **Face Match** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/facematch/node-match.js): Parallel processing of face **match** in multiple child worker threads
 - **Multiple Workers** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/multithread/node-multiprocess.js): Runs multiple parallel `human` by dispaching them to pool of pre-created worker processes  
 - **Dynamic Load** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/nodejs): Loads Human dynamically with multiple different desired backends  
 ## Project pages
 - [**Code Repository**](https://github.com/vladmandic/human)
 - [**NPM Package**](https://www.npmjs.com/package/@vladmandic/human)
 - [**Issues Tracker**](https://github.com/vladmandic/human/issues)
- [**TypeDoc API Specification**](https://vladmandic.github.io/human/typedoc/classes/Human.html)
+- [**TypeDoc API Specification - Main class**](https://vladmandic.github.io/human/typedoc/classes/Human.html)
 - [**TypeDoc API Specification - Full**](https://vladmandic.github.io/human/typedoc/)
 - [**Change Log**](https://github.com/vladmandic/human/blob/main/CHANGELOG.md)
 - [**Current To-do List**](https://github.com/vladmandic/human/blob/main/TODO.md)
@ -84,6 +116,7 @@ JavaScript module using TensorFlow/JS Machine Learning library
 - [**Usage & Functions**](https://github.com/vladmandic/human/wiki/Usage)
 - [**Configuration Details**](https://github.com/vladmandic/human/wiki/Config)
 - [**Result Details**](https://github.com/vladmandic/human/wiki/Result)
 - [**Customizing Draw Methods**](https://github.com/vladmandic/human/wiki/Draw)
 - [**Caching & Smoothing**](https://github.com/vladmandic/human/wiki/Caching)
 - [**Input Processing**](https://github.com/vladmandic/human/wiki/Image)
 - [**Face Recognition & Face Description**](https://github.com/vladmandic/human/wiki/Embedding)
@ -115,27 +148,24 @@ JavaScript module using TensorFlow/JS Machine Learning library
 <hr><br>
-## Examples
+## App Examples
-Visit [Examples galery](https://vladmandic.github.io/human/samples/samples.html) for more examples  
+Visit [Examples gallery](https://vladmandic.github.io/human/samples/index.html) for more examples  
-<https://vladmandic.github.io/human/samples/samples.html>
+[<img src="assets/samples.jpg" width="640"/>](assets/samples.jpg)
 ![samples](assets/samples.jpg)
 <br>
 ## Options
 All options as presented in the demo application...  
-> [demo/index.html](demo/index.html)  
+[demo/index.html](demo/index.html)  
-
+[<img src="assets/screenshot-menu.png"/>](assets/screenshot-menu.png)
 ![Options visible in demo](assets/screenshot-menu.png)
 <br>
 **Results Browser:**  
 [ *Demo -> Display -> Show Results* ]<br>
-![Results](assets/screenshot-results.png)
+[<img src="assets/screenshot-results.png"/>](assets/screenshot-results.png)
 <br>
@ -147,37 +177,47 @@ sorts them by similarity to selected face
 and optionally matches detected face with database of known people to guess their names
 > [demo/facematch](demo/facematch/index.html)  
-![Face Matching](assets/screenshot-facematch.jpg)
+[<img src="assets/screenshot-facematch.jpg" width="640"/>](assets/screenshot-facematch.jpg)
 2. **Face Detect:**  
 Extracts all detect faces from loaded images on-demand and highlights face details on a selected face  
 > [demo/facedetect](demo/facedetect/index.html)  
 [<img src="assets/screenshot-facedetect.jpg" width="640"/>](assets/screenshot-facedetect.jpg)
 3. **Face ID:**  
 Performs validation check on a webcam input to detect a real face and matches it to known faces stored in database
 > [demo/faceid](demo/faceid/index.html)  
 [<img src="assets/screenshot-faceid.jpg" width="640"/>](assets/screenshot-faceid.jpg)
 <br>
-2. **3D Rendering:**  
+4. **3D Rendering:**  
 > [human-motion](https://github.com/vladmandic/human-motion)
-![Face3D](https://github.com/vladmandic/human-motion/raw/main/assets/screenshot-face.jpg)
+[<img src="https://github.com/vladmandic/human-motion/raw/main/assets/screenshot-face.jpg" width="640"/>](https://github.com/vladmandic/human-motion/raw/main/assets/screenshot-face.jpg)
-![Body3D](https://github.com/vladmandic/human-motion/raw/main/assets/screenshot-body.jpg)
+[<img src="https://github.com/vladmandic/human-motion/raw/main/assets/screenshot-body.jpg" width="640"/>](https://github.com/vladmandic/human-motion/raw/main/assets/screenshot-body.jpg)
-![Hand3D](https://github.com/vladmandic/human-motion/raw/main/assets/screenshot-hand.jpg)
+[<img src="https://github.com/vladmandic/human-motion/raw/main/assets/screenshot-hand.jpg" width="640"/>](https://github.com/vladmandic/human-motion/raw/main/assets/screenshot-hand.jpg)
 <br>
-3. **Avatar Bone Mapping:**  
+5. **VR Model Tracking:**  
-> [human-avatar](https://github.com/vladmandic/human-avatar)
+> [human-three-vrm](https://github.com/vladmandic/human-three-vrm)  
 > [human-bjs-vrm](https://github.com/vladmandic/human-bjs-vrm)  
-![Avatar](https://github.com/vladmandic/human-avatar/raw/main/assets/screenshot.jpg)
+[<img src="https://github.com/vladmandic/human-three-vrm/raw/main/assets/human-vrm-screenshot.jpg" width="640"/>](https://github.com/vladmandic/human-three-vrm/raw/main/assets/human-vrm-screenshot.jpg)
 <br>
-4. **VR Model Tracking:**  
+6. **Human as OS native application:**
-> [human-vrmmotion](https://github.com/vladmandic/human-vrm)
+> [human-electron](https://github.com/vladmandic/human-electron)
 ![VRM](https://github.com/vladmandic/human-vrm/raw/main/assets/human-vrm-screenshot.jpg)
 <br>
 **468-Point Face Mesh Defails:**  
 (view in full resolution to see keypoints)  
-![FaceMesh](assets/facemesh.png)
+[<img src="assets/facemesh.png" width="400"/>](assets/facemesh.png)
 <br><hr><br>
@ -187,44 +227,25 @@ Simply load `Human` (*IIFE version*) directly from a cloud CDN in your HTML file
 (pick one: `jsdelirv`, `unpkg` or `cdnjs`)
 ```html
 <!DOCTYPE HTML>
 <script src="https://cdn.jsdelivr.net/npm/@vladmandic/human/dist/human.js"></script>
 <script src="https://unpkg.dev/@vladmandic/human/dist/human.js"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/human/2.1.5/human.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/human/3.0.0/human.js"></script>
 ```
 For details, including how to use `Browser ESM` version or `NodeJS` version of `Human`, see [**Installation**](https://github.com/vladmandic/human/wiki/Install)
 <br>
-## Inputs
+## Code Examples
-`Human` library can process all known input types:  
+Simple app that uses Human to process video input and  
 - `Image`, `ImageData`, `ImageBitmap`, `Canvas`, `OffscreenCanvas`, `Tensor`,  
 - `HTMLImageElement`, `HTMLCanvasElement`, `HTMLVideoElement`, `HTMLMediaElement`
 Additionally, `HTMLVideoElement`, `HTMLMediaElement` can be a standard `<video>` tag that links to:
 - WebCam on user's system
 - Any supported video type  
  For example: `.mp4`, `.avi`, etc.
 - Additional video types supported via *HTML5 Media Source Extensions*  
  Live streaming examples:
  - **HLS** (*HTTP Live Streaming*) using `hls.js`
  - **DASH** (Dynamic Adaptive Streaming over HTTP) using `dash.js`
 - **WebRTC** media track using built-in support  
 <br>
 ## Example
 Example simple app that uses Human to process video input and  
 draw output on screen using internal draw helper functions
 ```js
 // create instance of human with simple configuration using default values
 const config = { backend: 'webgl' };
-const human = new Human(config);
+const human = new Human.Human(config);
 // select input HTMLVideoElement and output HTMLCanvasElement from page
 const inputVideo = document.getElementById('video-id');
 const outputCanvas = document.getElementById('canvas-id');
@ -243,6 +264,7 @@ function detectVideo() {
    human.draw.gesture(outputCanvas, result.gesture);
    // and loop immediate to the next frame
    requestAnimationFrame(detectVideo);
    return result;
  });
 }
@ -303,7 +325,7 @@ async function detectVideo() {
 async function drawVideo() {
  if (result) { // check if result is available
-    const interpolated = human.next(result); // calculate next interpolated frame
+    const interpolated = human.next(result); // get smoothened result using last-known results
    human.draw.all(outputCanvas, interpolated); // draw the frame
  }
  requestAnimationFrame(drawVideo); // run draw loop
@ -313,26 +335,108 @@ detectVideo(); // start detection loop
 drawVideo(); // start draw loop
 ```
 or same, but using built-in full video processing instead of running manual frame-by-frame loop:
 ```js
 const human = new Human(); // create instance of Human
 const inputVideo = document.getElementById('video-id');
 const outputCanvas = document.getElementById('canvas-id');
 async function drawResults() {
  const interpolated = human.next(); // get smoothened result using last-known results
  human.draw.all(outputCanvas, interpolated); // draw the frame
  requestAnimationFrame(drawResults); // run draw loop
 }
 human.video(inputVideo); // start detection loop which continously updates results
 drawResults(); // start draw loop
 ```
 or using built-in webcam helper methods that take care of video handling completely:
 ```js
 const human = new Human(); // create instance of Human
 const outputCanvas = document.getElementById('canvas-id');
 async function drawResults() {
  const interpolated = human.next(); // get smoothened result using last-known results
  human.draw.canvas(outputCanvas, human.webcam.element); // draw current webcam frame
  human.draw.all(outputCanvas, interpolated); // draw the frame detectgion results
  requestAnimationFrame(drawResults); // run draw loop
 }
 await human.webcam.start({ crop: true });
 human.video(human.webcam.element); // start detection loop which continously updates results
 drawResults(); // start draw loop
 ```
 And for even better results, you can run detection in a separate web worker thread
 <br><hr><br>
 ## Inputs
 `Human` library can process all known input types:  
 - `Image`, `ImageData`, `ImageBitmap`, `Canvas`, `OffscreenCanvas`, `Tensor`,  
 - `HTMLImageElement`, `HTMLCanvasElement`, `HTMLVideoElement`, `HTMLMediaElement`
 Additionally, `HTMLVideoElement`, `HTMLMediaElement` can be a standard `<video>` tag that links to:
 - WebCam on user's system
 - Any supported video type  
  e.g. `.mp4`, `.avi`, etc.
 - Additional video types supported via *HTML5 Media Source Extensions*  
  e.g.: **HLS** (*HTTP Live Streaming*) using `hls.js` or **DASH** (*Dynamic Adaptive Streaming over HTTP*) using `dash.js`
 - **WebRTC** media track using built-in support  
 <br><hr><br>
 ## Detailed Usage
 - [**Wiki Home**](https://github.com/vladmandic/human/wiki)
 - [**List of all available methods, properies and namespaces**](https://github.com/vladmandic/human/wiki/Usage)
 - [**TypeDoc API Specification - Main class**](https://vladmandic.github.io/human/typedoc/classes/Human.html)
 - [**TypeDoc API Specification - Full**](https://vladmandic.github.io/human/typedoc/)
    ![typedoc](assets/screenshot-typedoc.png)
 <br><hr><br>
 ## TypeDefs
 `Human` is written using TypeScript strong typing and ships with full **TypeDefs** for all classes defined by the library bundled in `types/human.d.ts` and enabled by default  
 *Note*: This does not include embedded `tfjs`  
 If you want to use embedded `tfjs` inside `Human` (`human.tf` namespace) and still full **typedefs**, add this code:
 > import type * as tfjs from '@vladmandic/human/dist/tfjs.esm';  
 > const tf = human.tf as typeof tfjs;
 This is not enabled by default as `Human` does not ship with full **TFJS TypeDefs** due to size considerations  
 Enabling `tfjs` TypeDefs as above creates additional project (dev-only as only types are required) dependencies as defined in `@vladmandic/human/dist/tfjs.esm.d.ts`:
 > @tensorflow/tfjs-core, @tensorflow/tfjs-converter, @tensorflow/tfjs-backend-wasm, @tensorflow/tfjs-backend-webgl
 <br><hr><br>
 ## Default models
 Default models in Human library are:
- **Face Detection**: MediaPipe BlazeFace Back variation
+- **Face Detection**: *MediaPipe BlazeFace Back variation*
- **Face Mesh**: MediaPipe FaceMesh
+- **Face Mesh**: *MediaPipe FaceMesh*
- **Face Iris Analysis**: MediaPipe Iris
+- **Face Iris Analysis**: *MediaPipe Iris*
- **Face Description**: HSE FaceRes
+- **Face Description**: *HSE FaceRes*
- **Emotion Detection**: Oarriaga Emotion
+- **Emotion Detection**: *Oarriaga Emotion*
- **Body Analysis**: MoveNet Lightning variation
+- **Body Analysis**: *MoveNet Lightning variation*
- **Hand Analysis**: HandTrack & MediaPipe HandLandmarks
+- **Hand Analysis**: *HandTrack & MediaPipe HandLandmarks*
- **Body Segmentation**: Google Selfie
+- **Body Segmentation**: *Google Selfie*
- **Object Detection**: CenterNet with MobileNet v3
+- **Object Detection**: *CenterNet with MobileNet v3*
 Note that alternative models are provided and can be enabled via configuration  
-For example, `PoseNet` model can be switched for `BlazePose`, `EfficientPose` or `MoveNet` model depending on the use case  
+For example, body pose detection by default uses *MoveNet Lightning*, but can be switched to *MultiNet Thunder* for higher precision or *Multinet MultiPose* for multi-person detection or even *PoseNet*, *BlazePose* or *EfficientPose* depending on the use case  
 For more info, see [**Configuration Details**](https://github.com/vladmandic/human/wiki/Configuration) and [**List of Models**](https://github.com/vladmandic/human/wiki/Models)
@ -344,9 +448,9 @@ For more info, see [**Configuration Details**](https://github.com/vladmandic/hum
 <br><hr><br>
-`Human` library is written in `TypeScript` [4.5](https://www.typescriptlang.org/docs/handbook/intro.html)  
+`Human` library is written in [TypeScript](https://www.typescriptlang.org/docs/handbook/intro.html) **5.1** using [TensorFlow/JS](https://www.tensorflow.org/js/) **4.10** and conforming to latest `JavaScript` [ECMAScript version 2022](https://262.ecma-international.org/) standard  
-Conforming to latest `JavaScript` [ECMAScript version 2021](https://262.ecma-international.org/) standard  
+
-Build target is `JavaScript` [EMCAScript version 2018](https://262.ecma-international.org/11.0/)  
+Build target for distributables is `JavaScript` [EMCAScript version 2018](https://262.ecma-international.org/9.0/)  
 <br>
@ -355,6 +459,7 @@ and [**API Specification**](https://vladmandic.github.io/human/typedoc/classes/H
 <br>
 [![](https://img.shields.io/static/v1?label=Sponsor&message=%E2%9D%A4&logo=GitHub&color=%23fe8e86)](https://github.com/sponsors/vladmandic)
 ![Stars](https://img.shields.io/github/stars/vladmandic/human?style=flat-square&svg=true)
 ![Forks](https://badgen.net/github/forks/vladmandic/human)
 ![Code Size](https://img.shields.io/github/languages/code-size/vladmandic/human?style=flat-square&svg=true)
--- a/TODO.md
+++ b/TODO.md
@ -1,50 +1,38 @@
 # To-Do list for Human library
-## Work in Progress
+## Work-in-Progress
-<br>
+<hr><br>
-### Exploring
+## Known Issues & Limitations
- Optical flow: <https://docs.opencv.org/3.3.1/db/d7f/tutorial_js_lucas_kanade.html>
+### Face with Attention
 - Advanced histogram equalization: Adaptive, Contrast Limited, CLAHE
 - TFLite models: <https://js.tensorflow.org/api_tflite/0.0.1-alpha.4/>
 - Body segmentation: `robust-video-matting`
-<br><hr><br>
+`FaceMesh-Attention` is not supported when using `WASM` backend due to missing kernel op in **TFJS**  
 No issues with default model `FaceMesh`  
-## Known Issues
+### Object Detection
-#### WebGPU
+`NanoDet` model is not supported when using `WASM` backend due to missing kernel op in **TFJS**  
 No issues with default model `MB3-CenterNet`  
-Experimental support only until support is officially added in Chromium
+## Body Detection using MoveNet-MultiPose
-### Face Detection
+Model does not return valid detection scores (all other functionality is not impacted)  
-Enhanced rotation correction for face detection is not working in NodeJS due to missing kernel op in TFJS  
+### Firefox
 Feature is automatically disabled in NodeJS without user impact  
- Backend NodeJS missing kernel op `RotateWithOffset`  
+Running in **web workers** requires `OffscreenCanvas` which is still disabled by default in **Firefox**  
-  <https://github.com/tensorflow/tfjs/issues/5473>  
+Enable via `about:config` -> `gfx.offscreencanvas.enabled`  
 [Details](https://developer.mozilla.org/en-US/docs/Web/API/OffscreenCanvas#browser_compatibility)  
-<br><hr><br>
+### Safari
-## Human 2.6 Release Notes
+No support for running in **web workers** as Safari still does not support `OffscreenCanvas`  
 [Details](https://developer.mozilla.org/en-US/docs/Web/API/OffscreenCanvas#browser_compatibility)  
- Add model cache hander using **IndexDB** in *browser* environments  
+## React-Native
-  see `config.cacheModels` setting for details  
+
- Add additional demos  
+`Human` support for **React-Native** is best-effort, but not part of the main development focus  
-  `human-motion` and `human-avatar`  
+
- Updated samples image gallery  
+<hr><br>
 - Allow monkey-patching `fetch` in NodeJS  
  If `fetch` function is defined, it can be used to load models  
  from HTTP/HTTPS URLs regardless of `tfjs` platform support  
 - Fix `BlazeFace` detections when face is partially occluded  
 - Fix `BlazeFace` box scaling  
 - Fix `HandTrack` tracking when hand is in front of face  
 - Fix `ElectronJS` compatibility issues
 - Fix body keypoints interpolation algorithm
 - Updated `BlazePose` calculations  
 - Changes to `BlazePose` and `HandLandmark` annotations  
 - Strong typing for string enums  
 - Updated `TFJS`  
--- a/assets/screenshot-facedetect.jpg
+++ b/assets/screenshot-facedetect.jpg
--- a/assets/screenshot-faceid.jpg
+++ b/assets/screenshot-faceid.jpg
--- a/assets/screenshot-menu.png
+++ b/assets/screenshot-menu.png
--- a/assets/screenshot-results.png
+++ b/assets/screenshot-results.png
--- a/assets/screenshot-segmentation.jpg
+++ b/assets/screenshot-segmentation.jpg
--- a/assets/screenshot-typedoc.png
+++ b/assets/screenshot-typedoc.png
--- a/build.js
+++ b/build.js
@ -1,25 +1,127 @@
 const fs = require('fs');
-const log = require('@vladmandic/pilogger');
+const path = require('path');
-const Build = require('@vladmandic/build').Build;
+const log = require('@vladmandic/pilogger'); // eslint-disable-line node/no-unpublished-require
-const APIExtractor = require('@microsoft/api-extractor');
+const Build = require('@vladmandic/build').Build; // eslint-disable-line node/no-unpublished-require
 const APIExtractor = require('@microsoft/api-extractor'); // eslint-disable-line node/no-unpublished-require
 const tf = require('@tensorflow/tfjs-node'); // eslint-disable-line node/no-unpublished-require
 const packageJSON = require('./package.json');
-function copy(src, dst) {
+const logFile = 'test/build.log';
-  if (!fs.existsSync(src)) return;
+const modelsOut = 'models/models.json';
 const modelsFolders = [
  './models',
  '../human-models/models',
  '../blazepose/model/',
  '../anti-spoofing/model',
  '../efficientpose/models',
  '../insightface/models',
  '../movenet/models',
  '../nanodet/models',
 ];
 const apiExtractorIgnoreList = [ // eslint-disable-line no-unused-vars
  'ae-missing-release-tag',
  'tsdoc-param-tag-missing-hyphen',
  'tsdoc-escape-right-brace',
  'tsdoc-undefined-tag',
  'tsdoc-escape-greater-than',
  'ae-unresolved-link',
  'ae-forgotten-export',
  'tsdoc-malformed-inline-tag',
  'tsdoc-unnecessary-backslash',
 ];
 const regEx = [
  { search: 'types="@webgpu/types/dist"', replace: 'path="../src/types/webgpu.d.ts"' },
  { search: 'types="offscreencanvas"', replace: 'path="../src/types/offscreencanvas.d.ts"' },
 ];
 function copyFile(src, dst) {
  if (!fs.existsSync(src)) {
    log.warn('Copy:', { input: src, output: dst });
    return;
  }
  log.state('Copy:', { input: src, output: dst });
  const buffer = fs.readFileSync(src);
  fs.writeFileSync(dst, buffer);
 }
-const apiIgnoreList = ['ae-forgotten-export', 'ae-unresolved-link'];
+function writeFile(str, dst) {
  log.state('Write:', { output: dst });
  fs.writeFileSync(dst, str);
 }
 function regExFile(src, entries) {
  if (!fs.existsSync(src)) {
    log.warn('Filter:', { src });
    return;
  }
  log.state('Filter:', { input: src });
  for (const entry of entries) {
    const buffer = fs.readFileSync(src, 'UTF-8');
    const lines = buffer.split(/\r?\n/);
    const out = [];
    for (const line of lines) {
      if (line.includes(entry.search)) out.push(line.replace(entry.search, entry.replace));
      else out.push(line);
    }
    fs.writeFileSync(src, out.join('\n'));
  }
 }
 async function analyzeModels() {
  log.info('Analyze models:', { folders: modelsFolders.length, result: modelsOut });
  let totalSize = 0;
  const models = {};
  const allModels = [];
  for (const folder of modelsFolders) {
    try {
      if (!fs.existsSync(folder)) continue;
      const stat = fs.statSync(folder);
      if (!stat.isDirectory) continue;
      const dir = fs.readdirSync(folder);
      const found = dir.map((f) => `file://${folder}/${f}`).filter((f) => f.endsWith('json'));
      log.state('Models', { folder, models: found.length });
      allModels.push(...found);
    } catch {
      // log.warn('Cannot enumerate:', modelFolder);
    }
  }
  for (const url of allModels) {
    // if (!f.endsWith('.json')) continue;
    // const url = `file://${modelsDir}/${f}`;
    const model = new tf.GraphModel(url); // create model prototype and decide if load from cache or from original modelurl
    model.findIOHandler();
    const artifacts = await model.handler.load();
    const size = artifacts?.weightData?.byteLength || 0;
    totalSize += size;
    const name = path.basename(url).replace('.json', '');
    if (!models[name]) models[name] = size;
  }
  const json = JSON.stringify(models, null, 2);
  fs.writeFileSync(modelsOut, json);
  log.state('Models:', { count: Object.keys(models).length, totalSize });
 }
 async function main() {
  log.logFile(logFile);
  log.data('Build', { name: packageJSON.name, version: packageJSON.version });
  // run production build
  const build = new Build();
  await build.run('production');
  // patch tfjs typedefs
-  log.state('Copy:', { input: 'tfjs/tfjs.esm.d.ts' });
+  copyFile('node_modules/@vladmandic/tfjs/types/tfjs-core.d.ts', 'types/tfjs-core.d.ts');
-  copy('tfjs/tfjs.esm.d.ts', 'types/lib/dist/tfjs.esm.d.ts');
+  copyFile('node_modules/@vladmandic/tfjs/types/tfjs.d.ts', 'types/tfjs.esm.d.ts');
  copyFile('src/types/tsconfig.json', 'types/tsconfig.json');
  copyFile('src/types/eslint.json', 'types/.eslintrc.json');
  copyFile('src/types/tfjs.esm.d.ts', 'dist/tfjs.esm.d.ts');
  regExFile('types/tfjs-core.d.ts', regEx);
  // run api-extractor to create typedef rollup
-  const extractorConfig = APIExtractor.ExtractorConfig.loadFileAndPrepare('api-extractor.json');
+  const extractorConfig = APIExtractor.ExtractorConfig.loadFileAndPrepare('.api-extractor.json');
  try {
    const extractorResult = APIExtractor.Extractor.invoke(extractorConfig, {
      localBuild: true,
      showVerboseMessages: false,
@ -27,20 +129,25 @@ async function main() {
        msg.handled = true;
        if (msg.logLevel === 'none' || msg.logLevel === 'verbose' || msg.logLevel === 'info') return;
        if (msg.sourceFilePath?.includes('/node_modules/')) return;
-      if (apiIgnoreList.reduce((prev, curr) => prev || msg.messageId.includes(curr), false)) return;
+        // if (apiExtractorIgnoreList.reduce((prev, curr) => prev || msg.messageId.includes(curr), false)) return; // those are external issues outside of human control
        log.data('API', { level: msg.logLevel, category: msg.category, id: msg.messageId, file: msg.sourceFilePath, line: msg.sourceFileLine, text: msg.text });
      },
    });
    log.state('API-Extractor:', { succeeeded: extractorResult.succeeded, errors: extractorResult.errorCount, warnings: extractorResult.warningCount });
-  // distribute typedefs
+  } catch (err) {
-  log.state('Copy:', { input: 'types/human.d.ts' });
+    log.error('API-Extractor:', err);
-  copy('types/human.d.ts', 'dist/human.esm-nobundle.d.ts');
+  }
-  copy('types/human.d.ts', 'dist/human.esm.d.ts');
+  regExFile('types/human.d.ts', regEx);
-  copy('types/human.d.ts', 'dist/human.d.ts');
+  writeFile('export * from \'../types/human\';', 'dist/human.esm-nobundle.d.ts');
-  copy('types/human.d.ts', 'dist/human.node-gpu.d.ts');
+  writeFile('export * from \'../types/human\';', 'dist/human.esm.d.ts');
-  copy('types/human.d.ts', 'dist/human.node.d.ts');
+  writeFile('export * from \'../types/human\';', 'dist/human.d.ts');
-  copy('types/human.d.ts', 'dist/human.node-wasm.d.ts');
+  writeFile('export * from \'../types/human\';', 'dist/human.node-gpu.d.ts');
-  log.info('Human Build complete...');
+  writeFile('export * from \'../types/human\';', 'dist/human.node.d.ts');
  writeFile('export * from \'../types/human\';', 'dist/human.node-wasm.d.ts');
  // generate model signature
  await analyzeModels();
  log.info('Human Build complete...', { logFile });
 }
 main();
--- a/demo/README.md
+++ b/demo/README.md
@ -8,6 +8,7 @@ For details on other demos see Wiki: [**Demos**](https://github.com/vladmandic/h
 `index.html`: Full demo using `Human` ESM module running in Browsers,  
 Includes:
 - Selectable inputs:
  - Sample images
  - Image via drag & drop
@ -37,12 +38,14 @@ Includes:
  in `index.js:ui`
 ```js
 const ui = {
  console: true,      // log messages to browser console
  useWorker: true,    // use web workers for processing
  buffered: true,     // should output be buffered between frames
  interpolated: true, // should output be interpolated for smoothness between frames
  results: false,     // show results tree
  useWebRTC: false,   // use webrtc as camera source instead of local webcam
 };
 ```
 Demo implements several ways to use `Human` library,  
--- a/demo/benchmark/README.md
+++ b/demo/benchmark/README.md
@ -1,4 +0,0 @@
 # Human Benchmarks
 - `node.js` runs benchmark using `tensorflow` backend in **NodeJS**
 - `index.html` runs benchmark using `wasm`, `webgl`, `humangl` and `webgpu` backends in **Browser**
--- a/demo/benchmark/index.html
+++ b/demo/benchmark/index.html
@ -1,86 +0,0 @@
 <!DOCTYPE html>
 <html lang="en">
  <head>
    <meta charset="utf-8">
    <title>Human</title>
    <meta name="viewport" content="width=device-width" id="viewport">
    <meta name="keywords" content="Human">
    <meta name="application-name" content="Human">
    <meta name="description" content="Human: 3D Face Detection, Body Pose, Hand & Finger Tracking, Iris Tracking, Age & Gender Prediction, Emotion Prediction & Gesture Recognition; Author: Vladimir Mandic <https://github.com/vladmandic>">
    <meta name="msapplication-tooltip" content="Human: 3D Face Detection, Body Pose, Hand & Finger Tracking, Iris Tracking, Age & Gender Prediction, Emotion Prediction & Gesture Recognition; Author: Vladimir Mandic <https://github.com/vladmandic>">
    <meta name="theme-color" content="#000000">
    <link rel="manifest" href="../manifest.webmanifest">
    <link rel="shortcut icon" href="../../favicon.ico" type="image/x-icon">
    <link rel="apple-touch-icon" href="../../assets/icon.png">
    <style>
      @font-face { font-family: 'Lato'; font-display: swap; font-style: normal; font-weight: 100; src: local('Lato'), url('../../assets/lato-light.woff2') }
      html { font-family: 'Lato', 'Segoe UI'; font-size: 16px; font-variant: small-caps; }
      body { margin: 0; background: black; color: white; overflow-x: hidden; width: 100vw; height: 100vh; }
      body::-webkit-scrollbar { display: none; }
      .status { position: absolute; width: 100vw; bottom: 10%; text-align: center; font-size: 3rem; font-weight: 100; text-shadow: 2px 2px #303030; }
      .log { position: absolute; bottom: 0; margin: 0.4rem 0.4rem 0 0.4rem; font-size: 0.9rem; }
    </style>
  </head>
  <body>
    <div id="status" class="status"></div>
    <img id="image" src="../../samples/in/group-1.jpg" alt="test image" style="display: none">
    <div id="log" class="log"></div>
    <script type="module">
      import Human from '../../dist/human.esm.js';
      const loop = 20;
      const backends = ['wasm', 'webgl', 'humangl', 'webgpu'];
      // eslint-disable-next-line no-console
      const log = (...msg) => console.log(...msg);
      const myConfig = {
        modelBasePath: 'https://vladmandic.github.io/human/models',
        debug: true,
        async: true,
        cacheSensitivity: 0,
        filter: { enabled: false },
        face: {
          enabled: true,
          detector: { enabled: true, rotation: false },
          mesh: { enabled: true },
          iris: { enabled: true },
          description: { enabled: true },
          emotion: { enabled: false },
          antispoof: { enabled: true },
          liveness: { enabled: true },
        },
        hand: { enabled: true },
        body: { enabled: true },
        object: { enabled: true },
      };
      async function benchmark(backend) {
        myConfig.backend = backend;
        const human = new Human(myConfig);
        await human.tf.ready();
        log('Human:', human.version);
        await human.load();
        const loaded = Object.keys(human.models).filter((a) => human.models[a]);
        log('Loaded:', loaded);
        log('Memory state:', human.tf.engine().memory());
        const element = document.getElementById('image');
        const processed = await human.image(element);
        const t0 = human.now();
        await human.detect(processed.tensor, myConfig);
        const t1 = human.now();
        log('Backend:', human.tf.getBackend());
        log('Warmup:', Math.round(t1 - t0));
        for (let i = 0; i < loop; i++) await human.detect(processed.tensor, myConfig);
        const t2 = human.now();
        log('Average:', Math.round((t2 - t1) / loop));
      }
      async function main() {
        for (const backend of backends) await benchmark(backend);
      }
      window.onload = main;
    </script> 
  </body>
 </html>
--- a/demo/benchmark/node.js
+++ b/demo/benchmark/node.js
@ -1,65 +0,0 @@
 // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars
 const tf = require('@tensorflow/tfjs-node-gpu');
 const log = require('@vladmandic/pilogger');
 const canvasJS = require('canvas');
 const Human = require('../../dist/human.node-gpu.js').default;
 const input = './samples/in/group-1.jpg';
 const loop = 20;
 const myConfig = {
  backend: 'tensorflow',
  modelBasePath: 'https://vladmandic.github.io/human/models',
  wasmPath: 'https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@3.9.0/dist/',
  debug: true,
  async: true,
  cacheSensitivity: 0,
  filter: { enabled: false },
  face: {
    enabled: true,
    detector: { enabled: true, rotation: false },
    mesh: { enabled: true },
    iris: { enabled: true },
    description: { enabled: true },
    emotion: { enabled: true },
    antispoof: { enabled: true },
    liveness: { enabled: true },
  },
  hand: { enabled: true },
  body: { enabled: true },
  object: { enabled: true },
 };
 async function getImage(human) {
  const img = await canvasJS.loadImage(input);
  const canvas = canvasJS.createCanvas(img.width, img.height);
  const ctx = canvas.getContext('2d');
  ctx.drawImage(img, 0, 0, img.width, img.height);
  const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
  const tensor = human.tf.tensor(Array.from(imageData.data), [canvas.height, canvas.width, 4], 'int32'); // create rgba image tensor from flat array
  log.info('Image:', input, tensor.shape);
  return tensor;
 }
 async function main() {
  log.header();
  const human = new Human(myConfig);
  await human.tf.ready();
  log.info('Human:', human.version);
  await human.load();
  const loaded = Object.keys(human.models).filter((a) => human.models[a]);
  log.info('Loaded:', loaded);
  log.info('Memory state:', human.tf.engine().memory());
  const tensor = await getImage(human);
  log.state('Processing:', tensor['shape']);
  const t0 = human.now();
  await human.detect(tensor, myConfig);
  const t1 = human.now();
  log.state('Backend:', human.tf.getBackend());
  log.data('Warmup:', Math.round(t1 - t0));
  for (let i = 0; i < loop; i++) await human.detect(tensor, myConfig);
  const t2 = human.now();
  log.data('Average:', Math.round((t2 - t1) / loop));
 }
 main();
--- a/demo/facedetect/facedetect.js
+++ b/demo/facedetect/facedetect.js
@ -0,0 +1,160 @@
 /**
 * Human demo for browsers
 *
 * Demo for face detection
 */
 /** @type {Human} */
 import { Human } from '../../dist/human.esm.js';
 let loader;
 const humanConfig = { // user configuration for human, used to fine-tune behavior
  cacheSensitivity: 0,
  debug: true,
  modelBasePath: 'https://vladmandic.github.io/human-models/models/',
  filter: { enabled: true, equalization: false, flip: false },
  face: {
    enabled: true,
    detector: { rotation: false, maxDetected: 100, minConfidence: 0.2, return: true, square: false },
    iris: { enabled: true },
    description: { enabled: true },
    emotion: { enabled: true },
    antispoof: { enabled: true },
    liveness: { enabled: true },
  },
  body: { enabled: false },
  hand: { enabled: false },
  object: { enabled: false },
  gesture: { enabled: false },
  segmentation: { enabled: false },
 };
 const human = new Human(humanConfig); // new instance of human
 export const showLoader = (msg) => { loader.setAttribute('msg', msg); loader.style.display = 'block'; };
 export const hideLoader = () => loader.style.display = 'none';
 class ComponentLoader extends HTMLElement { // watch for attributes
  message = document.createElement('div');
  static get observedAttributes() { return ['msg']; }
  attributeChangedCallback(_name, _prevVal, currVal) {
    this.message.innerHTML = currVal;
  }
  connectedCallback() { // triggered on insert
    this.attachShadow({ mode: 'open' });
    const css = document.createElement('style');
    css.innerHTML = `
      .loader-container { top: 450px; justify-content: center; position: fixed; width: 100%; }
      .loader-message { font-size: 1.5rem; padding: 1rem; }
      .loader { width: 300px; height: 300px; border: 3px solid transparent; border-radius: 50%; border-top: 4px solid #f15e41; animation: spin 4s linear infinite; position: relative; }
      .loader::before, .loader::after { content: ""; position: absolute; top: 6px; bottom: 6px; left: 6px; right: 6px; border-radius: 50%; border: 4px solid transparent; }
      .loader::before { border-top-color: #bad375; animation: 3s spin linear infinite; }
      .loader::after { border-top-color: #26a9e0; animation: spin 1.5s linear infinite; }
      @keyframes spin { from { transform: rotate(0deg); } to { transform: rotate(360deg); } }
    `;
    const container = document.createElement('div');
    container.id = 'loader-container';
    container.className = 'loader-container';
    loader = document.createElement('div');
    loader.id = 'loader';
    loader.className = 'loader';
    this.message.id = 'loader-message';
    this.message.className = 'loader-message';
    this.message.innerHTML = '';
    container.appendChild(this.message);
    container.appendChild(loader);
    this.shadowRoot?.append(css, container);
    loader = this; // eslint-disable-line @typescript-eslint/no-this-alias
  }
 }
 customElements.define('component-loader', ComponentLoader);
 function addFace(face, source) {
  const deg = (rad) => Math.round((rad || 0) * 180 / Math.PI);
  const canvas = document.createElement('canvas');
  const emotion = face.emotion?.map((e) => `${Math.round(100 * e.score)}% ${e.emotion}`) || [];
  const rotation = `pitch ${deg(face.rotation?.angle.pitch)}° | roll ${deg(face.rotation?.angle.roll)}°  | yaw ${deg(face.rotation?.angle.yaw)}°`;
  const gaze = `direction ${deg(face.rotation?.gaze.bearing)}° strength ${Math.round(100 * (face.rotation.gaze.strength || 0))}%`;
  canvas.title = `
    source: ${source}
    score: ${Math.round(100 * face.boxScore)}% detection ${Math.round(100 * face.faceScore)}% analysis
    age: ${face.age} years | gender: ${face.gender} score ${Math.round(100 * face.genderScore)}%
    emotion: ${emotion.join(' | ')}
    head rotation: ${rotation}
    eyes gaze: ${gaze}
    camera distance: ${face.distance}m | ${Math.round(100 * face.distance / 2.54)}in
    check: ${Math.round(100 * face.real)}% real ${Math.round(100 * face.live)}% live
  `.replace(/  /g, ' ');
  canvas.onclick = (e) => {
    e.preventDefault();
    document.getElementById('description').innerHTML = canvas.title;
  };
  human.draw.tensor(face.tensor, canvas);
  human.tf.dispose(face.tensor);
  return canvas;
 }
 async function addFaces(imgEl) {
  showLoader('human: busy');
  const faceEl = document.getElementById('faces');
  faceEl.innerHTML = '';
  const res = await human.detect(imgEl);
  console.log(res); // eslint-disable-line no-console
  document.getElementById('description').innerHTML = `detected ${res.face.length} faces`;
  for (const face of res.face) {
    const canvas = addFace(face, imgEl.src.substring(0, 64));
    faceEl.appendChild(canvas);
  }
  hideLoader();
 }
 function addImage(imageUri) {
  const imgEl = new Image(256, 256);
  imgEl.onload = () => {
    const images = document.getElementById('images');
    images.appendChild(imgEl); // add image if loaded ok
    images.scroll(images?.offsetWidth, 0);
  };
  imgEl.onerror = () => console.error('addImage', { imageUri }); // eslint-disable-line no-console
  imgEl.onclick = () => addFaces(imgEl);
  imgEl.title = imageUri.substring(0, 64);
  imgEl.src = encodeURI(imageUri);
 }
 async function initDragAndDrop() {
  const reader = new FileReader();
  reader.onload = async (e) => {
    if (e.target.result.startsWith('data:image')) await addImage(e.target.result);
  };
  document.body.addEventListener('dragenter', (evt) => evt.preventDefault());
  document.body.addEventListener('dragleave', (evt) => evt.preventDefault());
  document.body.addEventListener('dragover', (evt) => evt.preventDefault());
  document.body.addEventListener('drop', async (evt) => {
    evt.preventDefault();
    evt.dataTransfer.dropEffect = 'copy';
    for (const f of evt.dataTransfer.files) reader.readAsDataURL(f);
  });
  document.body.onclick = (e) => {
    if (e.target.localName !== 'canvas') document.getElementById('description').innerHTML = '';
  };
 }
 async function main() {
  showLoader('loading models');
  await human.load();
  showLoader('compiling models');
  await human.warmup();
  showLoader('loading images');
  const images = ['group-1.jpg', 'group-2.jpg', 'group-3.jpg', 'group-4.jpg', 'group-5.jpg', 'group-6.jpg', 'group-7.jpg', 'solvay1927.jpg', 'stock-group-1.jpg', 'stock-group-2.jpg', 'stock-models-6.jpg', 'stock-models-7.jpg'];
  const imageUris = images.map((a) => `../../samples/in/${a}`);
  for (let i = 0; i < imageUris.length; i++) addImage(imageUris[i]);
  initDragAndDrop();
  hideLoader();
 }
 window.onload = main;
--- a/demo/facedetect/index.html
+++ b/demo/facedetect/index.html
@ -0,0 +1,43 @@
 <!DOCTYPE html>
 <html lang="en">
  <head>
    <meta charset="utf-8">
    <title>Human</title>
    <!-- <meta http-equiv="content-type" content="text/html; charset=utf-8"> -->
    <meta name="viewport" content="width=device-width, shrink-to-fit=yes">
    <meta name="keywords" content="Human">
    <meta name="application-name" content="Human">
    <meta name="description" content="Human: 3D Face Detection, Body Pose, Hand & Finger Tracking, Iris Tracking, Age & Gender Prediction, Emotion Prediction & Gesture Recognition; Author: Vladimir Mandic <https://github.com/vladmandic>">
    <meta name="msapplication-tooltip" content="Human: 3D Face Detection, Body Pose, Hand & Finger Tracking, Iris Tracking, Age & Gender Prediction, Emotion Prediction & Gesture Recognition; Author: Vladimir Mandic <https://github.com/vladmandic>">
    <meta name="theme-color" content="#000000">
    <link rel="manifest" href="../manifest.webmanifest">
    <link rel="shortcut icon" href="../../favicon.ico" type="image/x-icon">
    <link rel="apple-touch-icon" href="../../assets/icon.png">
    <script src="./facedetect.js" type="module"></script>
    <style>
      img { object-fit: contain; }
      img:hover { filter: grayscale(1); transform: scale(1.08); transition : all 0.3s ease; }   
      @font-face { font-family: 'Lato'; font-display: swap; font-style: normal; font-weight: 100; src: local('Lato'), url('../../assets/lato-light.woff2') }
      html { font-family: 'Lato', 'Segoe UI'; font-size: 24px; font-variant: small-caps; }
      body { margin: 24px; background: black; color: white; overflow: hidden; text-align: -webkit-center; width: 100vw; height: 100vh; }
      ::-webkit-scrollbar { height: 8px; border: 0; border-radius: 0; }
      ::-webkit-scrollbar-thumb { background: grey }
      ::-webkit-scrollbar-track { margin: 3px; }
      canvas { width: 192px; height: 192px; margin: 2px; padding: 2px; cursor: grab; transform: scale(1.00); transition : all 0.3s ease; }
      canvas:hover { filter: grayscale(1); transform: scale(1.08); transition : all 0.3s ease; }   
    </style>
  </head>
  <body>
    <component-loader></component-loader>
    <div style="display: flex">
      <div>
        <div style="margin: 24px">select image to show detected faces<br>drag & drop to add your images</div>
        <div id="images" style="display: flex; width: 98vw; overflow-x: auto; overflow-y: hidden; scroll-behavior: smooth"></div>
      </div>
    </div>
    <div id="list" style="height: 10px"></div>
    <div style="margin: 24px">hover or click on face to show details</div>
    <div id="faces" style="overflow-y: auto"></div>
    <div id="description" style="white-space: pre;"></div>
  </body>
 </html>
--- a/demo/faceid/README.md
+++ b/demo/faceid/README.md
@ -38,4 +38,5 @@ designed to serve as a quick check when used together with other indicators:
 **FaceID** is compatible with
 - `faceres.json` (default) perfoms combined age/gender/descriptor analysis
 - `faceres-deep.json` higher resolution variation of `faceres`
 - `insightface` alternative model for face descriptor analysis
 - `mobilefacenet` alternative model for face descriptor analysis
--- a/demo/faceid/index.html
+++ b/demo/faceid/index.html
@ -19,14 +19,23 @@
      body { margin: 0; padding: 16px; background: black; color: white; overflow-x: hidden; width: 100vw; height: 100vh; }
      body::-webkit-scrollbar { display: none; }
      .button { padding: 2px; cursor: pointer; box-shadow: 2px 2px black; width: 64px; text-align: center; place-content: center; margin-left: 16px; height: 16px; display: none }
-      .ok { position: absolute; top: 64px; right: 20px; width: 100px; background-color: grey; padding: 4px; color: black; font-size: 14px }
+      .ok { position: absolute; top: 64px; right: 20px; width: 150px; background-color: grey; padding: 4px; color: black; font-size: 14px }
    </style>
  </head>
  <body>
    <div style="padding: 8px">
      <h1 style="margin: 0">faceid demo using human library</h1>
      look directly at camera and make sure that detection passes all of the required tests noted on the right hand side of the screen<br>
      if input does not satisfies tests within specific timeout, no image will be selected<br>
      once face image is approved, it will be compared with existing face database<br>
      you can also store face descriptor with label in a browser's indexdb for future usage<br>
      <br>
      <i>note: this is not equivalent to full faceid methods as used by modern mobile phones or windows hello<br>
        as they rely on additional infrared sensors and depth-sensing and not just camera image for additional levels of security</i>
    </div>
    <canvas id="canvas" style="padding: 8px"></canvas>
    <canvas id="source" style="padding: 8px"></canvas>
    <video id="video" playsinline style="display: none"></video>
    <pre id="fps" style="position: absolute; bottom: 16px; right: 20px; background-color: grey; padding: 8px; box-shadow: 2px 2px black"></pre>
    <pre id="log" style="padding: 8px"></pre>
    <div id="match" style="display: none; padding: 8px">
      <label for="name">name:</label>
@ -34,7 +43,7 @@
      <span id="save" class="button" style="background-color: royalblue">save</span>
      <span id="delete" class="button" style="background-color: lightcoral">delete</span>
    </div>
-    <div id="retry" class="button" style="background-color: darkslategray; width: 350px; margin-top: 32px; padding: 4px">retry</div>
+    <div id="retry" class="button" style="background-color: darkslategray; width: 93%; margin-top: 32px; padding: 12px">retry</div>
    <div id="ok"></div>
  </body>
 </html>
--- a/demo/faceid/index.js
+++ b/demo/faceid/index.js
--- a/demo/faceid/index.js.map
+++ b/demo/faceid/index.js.map
--- a/demo/faceid/index.ts
+++ b/demo/faceid/index.ts
@ -7,17 +7,20 @@
 * @license MIT
 */
-import { Human, TensorLike, FaceResult } from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human
+import * as H from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human
 import * as indexDb from './indexdb'; // methods to deal with indexdb
 const humanConfig = { // user configuration for human, used to fine-tune behavior
  cacheSensitivity: 0.01,
  modelBasePath: '../../models',
-  filter: { equalization: true }, // lets run with histogram equilizer
+  filter: { enabled: true, equalization: true }, // lets run with histogram equilizer
  debug: true,
  face: {
    enabled: true,
-    detector: { rotation: true, return: true, cropFactor: 1.6, mask: false }, // return tensor is used to get detected face image
+    detector: { rotation: true, return: true, mask: false }, // return tensor is used to get detected face image
    description: { enabled: true }, // default model for face descriptor extraction is faceres
-    mobilefacenet: { enabled: false, modelPath: 'https://vladmandic.github.io/human-models/models/mobilefacenet.json' }, // alternative model
+    // mobilefacenet: { enabled: true, modelPath: 'https://vladmandic.github.io/human-models/models/mobilefacenet.json' }, // alternative model
    // insightface: { enabled: true, modelPath: 'https://vladmandic.github.io/insightface/models/insightface-mobilenet-swish.json' }, // alternative model
    iris: { enabled: true }, // needed to determine gaze direction
    emotion: { enabled: false }, // not needed
    antispoof: { enabled: true }, // enable optional antispoof module
@ -35,29 +38,50 @@ const matchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }; // for fa
 const options = {
  minConfidence: 0.6, // overal face confidence for box, face, gender, real, live
  minSize: 224, // min input to face descriptor model before degradation
-  maxTime: 10000, // max time before giving up
+  maxTime: 30000, // max time before giving up
  blinkMin: 10, // minimum duration of a valid blink
  blinkMax: 800, // maximum duration of a valid blink
  threshold: 0.5, // minimum similarity
  distanceMin: 0.4, // closest that face is allowed to be to the cammera in cm
  distanceMax: 1.0, // farthest that face is allowed to be to the cammera in cm
  mask: humanConfig.face.detector.mask,
  rotation: humanConfig.face.detector.rotation,
  cropFactor: humanConfig.face.detector.cropFactor,
  ...matchOptions,
 };
-const ok = { // must meet all rules
+const ok: Record<string, { status: boolean | undefined, val: number }> = { // must meet all rules
-  faceCount: false,
+  faceCount: { status: false, val: 0 },
-  faceConfidence: false,
+  faceConfidence: { status: false, val: 0 },
-  facingCenter: false,
+  facingCenter: { status: false, val: 0 },
-  lookingCenter: false,
+  lookingCenter: { status: false, val: 0 },
-  blinkDetected: false,
+  blinkDetected: { status: false, val: 0 },
-  faceSize: false,
+  faceSize: { status: false, val: 0 },
-  antispoofCheck: false,
+  antispoofCheck: { status: false, val: 0 },
-  livenessCheck: false,
+  livenessCheck: { status: false, val: 0 },
-  elapsedMs: 0, // total time while waiting for valid face
+  distance: { status: false, val: 0 },
  age: { status: false, val: 0 },
  gender: { status: false, val: 0 },
  timeout: { status: true, val: 0 },
  descriptor: { status: false, val: 0 },
  elapsedMs: { status: undefined, val: 0 }, // total time while waiting for valid face
  detectFPS: { status: undefined, val: 0 }, // mark detection fps performance
  drawFPS: { status: undefined, val: 0 }, // mark redraw fps performance
 };
-const allOk = () => ok.faceCount && ok.faceSize && ok.blinkDetected && ok.facingCenter && ok.lookingCenter && ok.faceConfidence && ok.antispoofCheck && ok.livenessCheck;
+
-const current: { face: FaceResult | null, record: indexDb.FaceRecord | null } = { face: null, record: null }; // current face record and matched database record
+const allOk = () => ok.faceCount.status
  && ok.faceSize.status
  && ok.blinkDetected.status
  && ok.facingCenter.status
  && ok.lookingCenter.status
  && ok.faceConfidence.status
  && ok.antispoofCheck.status
  && ok.livenessCheck.status
  && ok.distance.status
  && ok.descriptor.status
  && ok.age.status
  && ok.gender.status;
 const current: { face: H.FaceResult | null, record: indexDb.FaceRecord | null } = { face: null, record: null }; // current face record and matched database record
 const blink = { // internal timers for blink start/end/duration
  start: 0,
@ -66,9 +90,9 @@ const blink = { // internal timers for blink start/end/duration
 };
 // let db: Array<{ name: string, source: string, embedding: number[] }> = []; // holds loaded face descriptor database
-const human = new Human(humanConfig); // create instance of human with overrides from user configuration
+const human = new H.Human(humanConfig); // create instance of human with overrides from user configuration
-human.env['perfadd'] = false; // is performance data showing instant or total values
+human.env.perfadd = false; // is performance data showing instant or total values
 human.draw.options.font = 'small-caps 18px "Lato"'; // set font used to draw labels when using draw methods
 human.draw.options.lineHeight = 20;
@ -86,105 +110,119 @@ const dom = { // grab instances of dom objects so we dont have to look them up l
  ok: document.getElementById('ok') as HTMLDivElement,
 };
 const timestamp = { detect: 0, draw: 0 }; // holds information used to calculate performance and possible memory leaks
 const fps = { detect: 0, draw: 0 }; // holds calculated fps information for both detect and screen refresh
 let startTime = 0;
 const log = (...msg) => { // helper method to output messages
  dom.log.innerText += msg.join(' ') + '\n';
-  // eslint-disable-next-line no-console
+  console.log(...msg); // eslint-disable-line no-console
  console.log(...msg);
 };
 const printFPS = (msg) => dom.fps.innerText = msg; // print status element
 async function webCam() { // initialize webcam
  printFPS('starting webcam...');
  // @ts-ignore resizeMode is not yet defined in tslib
  const cameraOptions: MediaStreamConstraints = { audio: false, video: { facingMode: 'user', resizeMode: 'none', width: { ideal: document.body.clientWidth } } };
  const stream: MediaStream = await navigator.mediaDevices.getUserMedia(cameraOptions);
  const ready = new Promise((resolve) => { dom.video.onloadeddata = () => resolve(true); });
  dom.video.srcObject = stream;
-  dom.video.play();
+  void dom.video.play();
  await ready;
  dom.canvas.width = dom.video.videoWidth;
  dom.canvas.height = dom.video.videoHeight;
  dom.canvas.style.width = '50%';
  dom.canvas.style.height = '50%';
  if (human.env.initial) log('video:', dom.video.videoWidth, dom.video.videoHeight, '|', stream.getVideoTracks()[0].label);
  dom.canvas.onclick = () => { // pause when clicked on screen and resume on next click
-    if (dom.video.paused) dom.video.play();
+    if (dom.video.paused) void dom.video.play();
    else dom.video.pause();
  };
 }
 async function detectionLoop() { // main detection loop
  if (!dom.video.paused) {
-    if (current.face && current.face.tensor) human.tf.dispose(current.face.tensor); // dispose previous tensor
+    if (current.face?.tensor) human.tf.dispose(current.face.tensor); // dispose previous tensor
    await human.detect(dom.video); // actual detection; were not capturing output in a local variable as it can also be reached via human.result
    const now = human.now();
-    fps.detect = 1000 / (now - timestamp.detect);
+    ok.detectFPS.val = Math.round(10000 / (now - timestamp.detect)) / 10;
    timestamp.detect = now;
    requestAnimationFrame(detectionLoop); // start new frame immediately
  }
 }
-async function validationLoop(): Promise<FaceResult> { // main screen refresh loop
+function drawValidationTests() {
  const interpolated = await human.next(human.result); // smoothen result using last-known results
  await human.draw.canvas(dom.video, dom.canvas); // draw canvas to screen
  await human.draw.all(dom.canvas, interpolated); // draw labels, boxes, lines, etc.
  const now = human.now();
  fps.draw = 1000 / (now - timestamp.draw);
  timestamp.draw = now;
  printFPS(`fps: ${fps.detect.toFixed(1).padStart(5, ' ')} detect | ${fps.draw.toFixed(1).padStart(5, ' ')} draw`); // write status
  ok.faceCount = human.result.face.length === 1; // must be exactly detected face
  if (ok.faceCount) { // skip the rest if no face
    const gestures: string[] = Object.values(human.result.gesture).map((gesture) => gesture.gesture); // flatten all gestures
    if (gestures.includes('blink left eye') || gestures.includes('blink right eye')) blink.start = human.now(); // blink starts when eyes get closed
    if (blink.start > 0 && !gestures.includes('blink left eye') && !gestures.includes('blink right eye')) blink.end = human.now(); // if blink started how long until eyes are back open
    ok.blinkDetected = ok.blinkDetected || (Math.abs(blink.end - blink.start) > options.blinkMin && Math.abs(blink.end - blink.start) < options.blinkMax);
    if (ok.blinkDetected && blink.time === 0) blink.time = Math.trunc(blink.end - blink.start);
    ok.facingCenter = gestures.includes('facing center');
    ok.lookingCenter = gestures.includes('looking center'); // must face camera and look at camera
    ok.faceConfidence = (human.result.face[0].boxScore || 0) > options.minConfidence && (human.result.face[0].faceScore || 0) > options.minConfidence && (human.result.face[0].genderScore || 0) > options.minConfidence;
    ok.antispoofCheck = (human.result.face[0].real || 0) > options.minConfidence;
    ok.livenessCheck = (human.result.face[0].live || 0) > options.minConfidence;
    ok.faceSize = human.result.face[0].box[2] >= options.minSize && human.result.face[0].box[3] >= options.minSize;
  }
  let y = 32;
  for (const [key, val] of Object.entries(ok)) {
    let el = document.getElementById(`ok-${key}`);
    if (!el) {
      el = document.createElement('div');
      el.id = `ok-${key}`;
      el.innerText = key;
      el.className = 'ok';
      el.style.top = `${y}px`;
      dom.ok.appendChild(el);
    }
-    if (typeof val === 'boolean') el.style.backgroundColor = val ? 'lightgreen' : 'lightcoral';
+    if (typeof val.status === 'boolean') el.style.backgroundColor = val.status ? 'lightgreen' : 'lightcoral';
-    else el.innerText = `${key}:${val}`;
+    const status = val.status ? 'ok' : 'fail';
    el.innerText = `${key}: ${val.val === 0 ? status : val.val}`;
    y += 28;
  }
-  if (allOk()) { // all criteria met
+}
 async function validationLoop(): Promise<H.FaceResult> { // main screen refresh loop
  const interpolated = human.next(human.result); // smoothen result using last-known results
  human.draw.canvas(dom.video, dom.canvas); // draw canvas to screen
  await human.draw.all(dom.canvas, interpolated); // draw labels, boxes, lines, etc.
  const now = human.now();
  ok.drawFPS.val = Math.round(10000 / (now - timestamp.draw)) / 10;
  timestamp.draw = now;
  ok.faceCount.val = human.result.face.length;
  ok.faceCount.status = ok.faceCount.val === 1; // must be exactly detected face
  if (ok.faceCount.status) { // skip the rest if no face
    const gestures: string[] = Object.values(human.result.gesture).map((gesture: H.GestureResult) => gesture.gesture); // flatten all gestures
    if (gestures.includes('blink left eye') || gestures.includes('blink right eye')) blink.start = human.now(); // blink starts when eyes get closed
    if (blink.start > 0 && !gestures.includes('blink left eye') && !gestures.includes('blink right eye')) blink.end = human.now(); // if blink started how long until eyes are back open
    ok.blinkDetected.status = ok.blinkDetected.status || (Math.abs(blink.end - blink.start) > options.blinkMin && Math.abs(blink.end - blink.start) < options.blinkMax);
    if (ok.blinkDetected.status && blink.time === 0) blink.time = Math.trunc(blink.end - blink.start);
    ok.facingCenter.status = gestures.includes('facing center');
    ok.lookingCenter.status = gestures.includes('looking center'); // must face camera and look at camera
    ok.faceConfidence.val = human.result.face[0].faceScore || human.result.face[0].boxScore || 0;
    ok.faceConfidence.status = ok.faceConfidence.val >= options.minConfidence;
    ok.antispoofCheck.val = human.result.face[0].real || 0;
    ok.antispoofCheck.status = ok.antispoofCheck.val >= options.minConfidence;
    ok.livenessCheck.val = human.result.face[0].live || 0;
    ok.livenessCheck.status = ok.livenessCheck.val >= options.minConfidence;
    ok.faceSize.val = Math.min(human.result.face[0].box[2], human.result.face[0].box[3]);
    ok.faceSize.status = ok.faceSize.val >= options.minSize;
    ok.distance.val = human.result.face[0].distance || 0;
    ok.distance.status = (ok.distance.val >= options.distanceMin) && (ok.distance.val <= options.distanceMax);
    ok.descriptor.val = human.result.face[0].embedding?.length || 0;
    ok.descriptor.status = ok.descriptor.val > 0;
    ok.age.val = human.result.face[0].age || 0;
    ok.age.status = ok.age.val > 0;
    ok.gender.val = human.result.face[0].genderScore || 0;
    ok.gender.status = ok.gender.val >= options.minConfidence;
  }
  // run again
  ok.timeout.status = ok.elapsedMs.val <= options.maxTime;
  drawValidationTests();
  if (allOk() || !ok.timeout.status) { // all criteria met
    dom.video.pause();
    return human.result.face[0];
  }
-  if (ok.elapsedMs > options.maxTime) { // give up
+  ok.elapsedMs.val = Math.trunc(human.now() - startTime);
    dom.video.pause();
    return human.result.face[0];
  } else { // run again
    ok.elapsedMs = Math.trunc(human.now() - startTime);
  return new Promise((resolve) => {
    setTimeout(async () => {
-        const res = await validationLoop(); // run validation loop until conditions are met
+      await validationLoop(); // run validation loop until conditions are met
-        if (res) resolve(human.result.face[0]); // recursive promise resolve
+      resolve(human.result.face[0]); // recursive promise resolve
    }, 30); // use to slow down refresh from max refresh rate to target of 30 fps
  });
 }
 }
 async function saveRecords() {
  if (dom.name.value.length > 0) {
    const image = dom.canvas.getContext('2d')?.getImageData(0, 0, dom.canvas.width, dom.canvas.height) as ImageData;
    const rec = { id: 0, name: dom.name.value, descriptor: current.face?.embedding as number[], image };
    await indexDb.save(rec);
-    log('saved face record:', rec.name);
+    log('saved face record:', rec.name, 'descriptor length:', current.face?.embedding?.length);
    log('known face records:', await indexDb.count());
  } else {
    log('invalid name');
  }
@ -197,20 +235,21 @@ async function deleteRecord() {
 }
 async function detectFace() {
  dom.canvas.style.height = '';
  dom.canvas.getContext('2d')?.clearRect(0, 0, options.minSize, options.minSize);
-  if (!current.face || !current.face.tensor || !current.face.embedding) return false;
+  if (!current?.face?.tensor || !current?.face?.embedding) return false;
-  // eslint-disable-next-line no-console
+  console.log('face record:', current.face); // eslint-disable-line no-console
-  console.log('face record:', current.face);
+  log(`detected face: ${current.face.gender} ${current.face.age || 0}y distance ${100 * (current.face.distance || 0)}cm/${Math.round(100 * (current.face.distance || 0) / 2.54)}in`);
-  human.tf.browser.toPixels(current.face.tensor as unknown as TensorLike, dom.canvas);
+  await human.draw.tensor(current.face.tensor, dom.canvas);
  if (await indexDb.count() === 0) {
-    log('face database is empty');
+    log('face database is empty: nothing to compare face with');
    document.body.style.background = 'black';
    dom.delete.style.display = 'none';
    return false;
  }
  const db = await indexDb.load();
-  const descriptors = db.map((rec) => rec.descriptor);
+  const descriptors = db.map((rec) => rec.descriptor).filter((desc) => desc.length > 0);
-  const res = await human.match(current.face.embedding, descriptors, matchOptions);
+  const res = human.match.find(current.face.embedding, descriptors, matchOptions);
  current.record = db[res.index] || null;
  if (current.record) {
    log(`best match: ${current.record.name} | id: ${current.record.id} | similarity: ${Math.round(1000 * res.similarity) / 10}%`);
@ -223,17 +262,20 @@ async function detectFace() {
 }
 async function main() { // main entry point
-  ok.faceCount = false;
+  ok.faceCount.status = false;
-  ok.faceConfidence = false;
+  ok.faceConfidence.status = false;
-  ok.facingCenter = false;
+  ok.facingCenter.status = false;
-  ok.blinkDetected = false;
+  ok.blinkDetected.status = false;
-  ok.faceSize = false;
+  ok.faceSize.status = false;
-  ok.antispoofCheck = false;
+  ok.antispoofCheck.status = false;
-  ok.livenessCheck = false;
+  ok.livenessCheck.status = false;
-  ok.elapsedMs = 0;
+  ok.age.status = false;
  ok.gender.status = false;
  ok.elapsedMs.val = 0;
  dom.match.style.display = 'none';
  dom.retry.style.display = 'none';
  dom.source.style.display = 'none';
  dom.canvas.style.height = '50%';
  document.body.style.background = 'black';
  await webCam();
  await detectionLoop(); // start detection loop
@ -251,19 +293,21 @@ async function main() { // main entry point
  if (!allOk()) { // is all criteria met?
    log('did not find valid face');
    return false;
  } else {
    return detectFace();
  }
  return detectFace();
 }
 async function init() {
  log('human version:', human.version, '| tfjs version:', human.tf.version['tfjs-core']);
  log('options:', JSON.stringify(options).replace(/{|}|"|\[|\]/g, '').replace(/,/g, ' '));
-  printFPS('loading...');
+  log('initializing webcam...');
  log('known face records:', await indexDb.count());
  await webCam(); // start webcam
  log('loading human models...');
  await human.load(); // preload all models
-  printFPS('initializing...');
+  log('initializing human...');
  log('face embedding model:', humanConfig.face.description.enabled ? 'faceres' : '', humanConfig.face['mobilefacenet']?.enabled ? 'mobilefacenet' : '', humanConfig.face['insightface']?.enabled ? 'insightface' : '');
  log('loading face database...');
  log('known face records:', await indexDb.count());
  dom.retry.addEventListener('click', main);
  dom.save.addEventListener('click', saveRecords);
  dom.delete.addEventListener('click', deleteRecord);
--- a/demo/faceid/indexdb.ts
+++ b/demo/faceid/indexdb.ts
@ -3,10 +3,9 @@ let db: IDBDatabase; // instance of indexdb
 const database = 'human';
 const table = 'person';
-export type FaceRecord = { id: number, name: string, descriptor: number[], image: ImageData };
+export interface FaceRecord { id: number, name: string, descriptor: number[], image: ImageData }
-// eslint-disable-next-line no-console
+const log = (...msg) => console.log('indexdb', ...msg); // eslint-disable-line no-console
 const log = (...msg) => console.log('indexdb', ...msg);
 export async function open() {
  if (db) return true;
@ -19,7 +18,7 @@ export async function open() {
      db.createObjectStore(table, { keyPath: 'id', autoIncrement: true });
    };
    request.onsuccess = (evt) => { // open
-      db = (evt.target as IDBOpenDBRequest).result as IDBDatabase;
+      db = (evt.target as IDBOpenDBRequest).result;
      log('open:', db);
      resolve(true);
    };
@ -27,7 +26,7 @@ export async function open() {
 }
 export async function load(): Promise<FaceRecord[]> {
-  const faceDB: Array<FaceRecord> = [];
+  const faceDB: FaceRecord[] = [];
  if (!db) await open(); // open or create if not already done
  return new Promise((resolve) => {
    const cursor: IDBRequest = db.transaction([table], 'readwrite').objectStore(table).openCursor(null, 'next');
--- a/demo/facematch/README.md
+++ b/demo/facematch/README.md
@ -11,7 +11,7 @@
 ## Browser Face Recognition Demo
 - `demo/facematch`: Demo for Browsers that uses all face description and embedding features to  
-detect, extract and identify all faces plus calculate simmilarity between them
+detect, extract and identify all faces plus calculate similarity between them
 It highlights functionality such as:
@ -72,12 +72,13 @@ Non-linear performance that increases with number of worker threads due to commu
 > node node-match
 <!-- eslint-skip -->
 ```js
-2021-10-13 07:53:36 INFO:  options: { dbFile: './faces.json', dbMax: 10000, threadPoolSize: 6, workerSrc: './node-match-worker.js', debug: false, minThreshold: 0.9, descLength: 1024 }
+INFO:  options: { dbFile: './faces.json', dbMax: 10000, threadPoolSize: 6, workerSrc: './node-match-worker.js', debug: false, minThreshold: 0.9, descLength: 1024 }
-2021-10-13 07:53:36 DATA:  created shared buffer: { maxDescriptors: 10000, totalBytes: 40960000, totalElements: 10240000 }
+DATA:  created shared buffer: { maxDescriptors: 10000, totalBytes: 40960000, totalElements: 10240000 }
-2021-10-13 07:53:36 DATA:  db loaded: { existingRecords: 0, newRecords: 5700 }
+DATA:  db loaded: { existingRecords: 0, newRecords: 5700 }
-2021-10-13 07:53:36 INFO:  starting worker thread pool: { totalWorkers: 6, alreadyActive: 0 }
+INFO:  starting worker thread pool: { totalWorkers: 6, alreadyActive: 0 }
-2021-10-13 07:53:36 STATE: submitted: { matchJobs: 100, poolSize: 6, activeWorkers: 6 }
+STATE: submitted: { matchJobs: 100, poolSize: 6, activeWorkers: 6 }
-2021-10-13 07:53:38 STATE: { matchJobsFinished: 100, totalTimeMs: 1769, averageTimeMs: 17.69 }
+STATE: { matchJobsFinished: 100, totalTimeMs: 1769, averageTimeMs: 17.69 }
-2021-10-13 07:53:38 INFO:  closing workers: { poolSize: 6, activeWorkers: 6 }
+INFO:  closing workers: { poolSize: 6, activeWorkers: 6 }
 ```
--- a/demo/facematch/facematch.js
+++ b/demo/facematch/facematch.js
@ -1,18 +1,17 @@
 // @ts-nocheck
 /**
 * Human demo for browsers
 *
- * Demo for face descriptor analysis and face simmilarity analysis
+ * Demo for face descriptor analysis and face similarity analysis
 */
 /** @type {Human} */
-import Human from '../../dist/human.esm.js';
+import { Human } from '../../dist/human.esm.js';
 const userConfig = {
  backend: 'humangl',
  async: true,
  warmup: 'none',
-  cacheSensitivity: 0,
+  cacheSensitivity: 0.01,
  debug: true,
  modelBasePath: '../../models/',
  deallocate: true,
@ -23,7 +22,6 @@ const userConfig = {
  },
  face: {
    enabled: true,
    // detector: { rotation: false, return: true, maxDetected: 50, iouThreshold: 0.206, minConfidence: 0.122 },
    detector: { return: true, rotation: true, maxDetected: 50, iouThreshold: 0.01, minConfidence: 0.2 },
    mesh: { enabled: true },
    iris: { enabled: false },
@ -46,8 +44,7 @@ const minScore = 0.4;
 function log(...msg) {
  const dt = new Date();
  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;
-  // eslint-disable-next-line no-console
+  console.log(ts, ...msg); // eslint-disable-line no-console
  console.log(ts, ...msg);
 }
 function title(msg) {
@ -66,25 +63,16 @@ async function loadFaceMatchDB() {
  }
 }
-async function SelectFaceCanvas(face) {
+async function selectFaceCanvas(face) {
  // if we have face image tensor, enhance it and display it
  let embedding;
  document.getElementById('orig').style.filter = 'blur(16px)';
  if (face.tensor) {
    title('Sorting Faces by Similarity');
    const enhanced = human.enhance(face);
    if (enhanced) {
    const c = document.getElementById('orig');
-      const squeeze = human.tf.squeeze(enhanced);
+    await human.draw.tensor(face.tensor, c);
      const normalize = human.tf.div(squeeze, 255);
      await human.tf.browser.toPixels(normalize, c);
      human.tf.dispose([enhanced, squeeze, normalize]);
      const ctx = c.getContext('2d');
      ctx.font = 'small-caps 0.4rem "Lato"';
      ctx.fillStyle = 'rgba(255, 255, 255, 1)';
    }
    const arr = db.map((rec) => rec.embedding);
-    const res = await human.match(face.embedding, arr);
+    const res = await human.match.find(face.embedding, arr);
    log('Match:', db[res.index].name);
    const emotion = face.emotion[0] ? `${Math.round(100 * face.emotion[0].score)}% ${face.emotion[0].emotion}` : 'N/A';
    document.getElementById('desc').innerHTML = `
@ -105,11 +93,11 @@ async function SelectFaceCanvas(face) {
  for (const canvas of canvases) {
    // calculate similarity from selected face to current one in the loop
    const current = all[canvas.tag.sample][canvas.tag.face];
-    const similarity = human.similarity(face.embedding, current.embedding);
+    const similarity = human.match.similarity(face.embedding, current.embedding);
    canvas.tag.similarity = similarity;
    // get best match
    // draw the canvas
-    await human.tf.browser.toPixels(current.tensor, canvas);
+    await human.draw.tensor(current.tensor, canvas);
    const ctx = canvas.getContext('2d');
    ctx.font = 'small-caps 1rem "Lato"';
    ctx.fillStyle = 'rgba(0, 0, 0, 1)';
@ -122,7 +110,7 @@ async function SelectFaceCanvas(face) {
    ctx.font = 'small-caps 1rem "Lato"';
    const start = human.now();
    const arr = db.map((rec) => rec.embedding);
-    const res = await human.match(current.embedding, arr);
+    const res = await human.match.find(current.embedding, arr);
    time += (human.now() - start);
    if (res.similarity > minScore) ctx.fillText(`DB: ${(100 * res.similarity).toFixed(1)}% ${db[res.index].name}`, 4, canvas.height - 30);
  }
@ -137,7 +125,7 @@ async function SelectFaceCanvas(face) {
  title('Selected Face');
 }
-async function AddFaceCanvas(index, res, fileName) {
+async function addFaceCanvas(index, res, fileName) {
  all[index] = res.face;
  for (const i in res.face) {
    if (!res.face[i].tensor) continue; // did not get valid results
@ -156,35 +144,37 @@ async function AddFaceCanvas(index, res, fileName) {
      gender: ${Math.round(100 * res.face[i].genderScore)}% ${res.face[i].gender}
      emotion: ${emotion}
    `.replace(/  /g, ' ');
-    await human.tf.browser.toPixels(res.face[i].tensor, canvas);
+    await human.draw.tensor(res.face[i].tensor, canvas);
    const ctx = canvas.getContext('2d');
-    if (!ctx) return false;
+    if (!ctx) return;
    ctx.font = 'small-caps 0.8rem "Lato"';
    ctx.fillStyle = 'rgba(255, 255, 255, 1)';
    ctx.fillText(`${res.face[i].age}y ${(100 * (res.face[i].genderScore || 0)).toFixed(1)}% ${res.face[i].gender}`, 4, canvas.height - 6);
    const arr = db.map((rec) => rec.embedding);
-    const result = human.match(res.face[i].embedding, arr);
+    const result = human.match.find(res.face[i].embedding, arr);
    ctx.font = 'small-caps 1rem "Lato"';
    if (result.similarity && res.similarity > minScore) ctx.fillText(`${(100 * result.similarity).toFixed(1)}% ${db[result.index].name}`, 4, canvas.height - 30);
    document.getElementById('faces').appendChild(canvas);
    canvas.addEventListener('click', (evt) => {
      log('Select:', 'Image:', evt.target.tag.sample, 'Face:', evt.target.tag.face, 'Source:', evt.target.tag.source, all[evt.target.tag.sample][evt.target.tag.face]);
-      SelectFaceCanvas(all[evt.target.tag.sample][evt.target.tag.face]);
+      selectFaceCanvas(all[evt.target.tag.sample][evt.target.tag.face]);
    });
  }
 }
-async function AddImageElement(index, image, length) {
+async function addImageElement(index, image, length) {
  const faces = all.reduce((prev, curr) => prev += curr.length, 0);
  title(`Analyzing Input Images<br> ${Math.round(100 * index / length)}% [${index} / ${length}]<br>Found ${faces} Faces`);
  return new Promise((resolve) => {
    const img = new Image(128, 128);
    img.onload = () => { // must wait until image is loaded
      document.getElementById('images').appendChild(img); // and finally we can add it
-      human.detect(img, userConfig).then((res) => {
+      human.detect(img, userConfig)
-        AddFaceCanvas(index, res, image); // then wait until image is analyzed
+        .then((res) => { // eslint-disable-line promise/always-return
          addFaceCanvas(index, res, image); // then wait until image is analyzed
          resolve(true);
-      });
+        })
        .catch(() => log('human detect error'));
    };
    img.onerror = () => {
      log('Add image error:', index + 1, image);
@ -222,18 +212,23 @@ async function main() {
  // could not dynamically enumerate images so using static list
  if (images.length === 0) {
    images = [
-      'ai-body.jpg', 'solvay1927.jpg', 'ai-upper.jpg',
+      'ai-face.jpg', 'ai-upper.jpg', 'ai-body.jpg', 'solvay1927.jpg',
      'person-carolina.jpg', 'person-celeste.jpg', 'person-leila1.jpg', 'person-leila2.jpg', 'person-lexi.jpg', 'person-linda.jpg', 'person-nicole.jpg', 'person-tasia.jpg',
      'person-tetiana.jpg', 'person-vlado1.jpg', 'person-vlado5.jpg', 'person-vlado.jpg', 'person-christina.jpg', 'person-lauren.jpg',
      'group-1.jpg', 'group-2.jpg', 'group-3.jpg', 'group-4.jpg', 'group-5.jpg', 'group-6.jpg', 'group-7.jpg',
-      'daz3d-brianna.jpg', 'daz3d-chiyo.jpg', 'daz3d-cody.jpg', 'daz3d-drew-01.jpg', 'daz3d-drew-02.jpg', 'daz3d-ella-01.jpg', 'daz3d-ella-02.jpg', 'daz3d-gillian.jpg',
+      'person-celeste.jpg', 'person-christina.jpg', 'person-lauren.jpg', 'person-lexi.jpg', 'person-linda.jpg', 'person-nicole.jpg', 'person-tasia.jpg', 'person-tetiana.jpg', 'person-vlado.jpg', 'person-vlado1.jpg', 'person-vlado5.jpg',
-      'daz3d-hye-01.jpg', 'daz3d-hye-02.jpg', 'daz3d-kaia.jpg', 'daz3d-karen.jpg', 'daz3d-kiaria-01.jpg', 'daz3d-kiaria-02.jpg', 'daz3d-lilah-01.jpg', 'daz3d-lilah-02.jpg',
+      'stock-group-1.jpg', 'stock-group-2.jpg',
-      'daz3d-lilah-03.jpg', 'daz3d-lila.jpg', 'daz3d-lindsey.jpg', 'daz3d-megah.jpg', 'daz3d-selina-01.jpg', 'daz3d-selina-02.jpg', 'daz3d-snow.jpg',
+      'stock-models-1.jpg', 'stock-models-2.jpg', 'stock-models-3.jpg', 'stock-models-4.jpg', 'stock-models-5.jpg', 'stock-models-6.jpg', 'stock-models-7.jpg', 'stock-models-8.jpg', 'stock-models-9.jpg',
-      'daz3d-sunshine.jpg', 'daz3d-taia.jpg', 'daz3d-tuesday-01.jpg', 'daz3d-tuesday-02.jpg', 'daz3d-tuesday-03.jpg', 'daz3d-zoe.jpg', 'daz3d-ginnifer.jpg',
+      'stock-teen-1.jpg', 'stock-teen-2.jpg', 'stock-teen-3.jpg', 'stock-teen-4.jpg', 'stock-teen-5.jpg', 'stock-teen-6.jpg', 'stock-teen-7.jpg', 'stock-teen-8.jpg',
-      'daz3d-_emotions01.jpg', 'daz3d-_emotions02.jpg', 'daz3d-_emotions03.jpg', 'daz3d-_emotions04.jpg', 'daz3d-_emotions05.jpg',
+      'stock-models-10.jpg', 'stock-models-11.jpg', 'stock-models-12.jpg', 'stock-models-13.jpg', 'stock-models-14.jpg', 'stock-models-15.jpg', 'stock-models-16.jpg',
      'cgi-model-1.jpg', 'cgi-model-2.jpg', 'cgi-model-3.jpg', 'cgi-model-4.jpg', 'cgi-model-5.jpg', 'cgi-model-6.jpg', 'cgi-model-7.jpg', 'cgi-model-8.jpg', 'cgi-model-9.jpg',
      'cgi-model-10.jpg', 'cgi-model-11.jpg', 'cgi-model-12.jpg', 'cgi-model-13.jpg', 'cgi-model-14.jpg', 'cgi-model-15.jpg', 'cgi-model-18.jpg', 'cgi-model-19.jpg',
      'cgi-model-20.jpg', 'cgi-model-21.jpg', 'cgi-model-22.jpg', 'cgi-model-23.jpg', 'cgi-model-24.jpg', 'cgi-model-25.jpg', 'cgi-model-26.jpg', 'cgi-model-27.jpg', 'cgi-model-28.jpg', 'cgi-model-29.jpg',
      'cgi-model-30.jpg', 'cgi-model-31.jpg', 'cgi-model-33.jpg', 'cgi-model-34.jpg',
      'cgi-multiangle-1.jpg', 'cgi-multiangle-2.jpg', 'cgi-multiangle-3.jpg', 'cgi-multiangle-4.jpg', 'cgi-multiangle-6.jpg', 'cgi-multiangle-7.jpg', 'cgi-multiangle-8.jpg', 'cgi-multiangle-9.jpg', 'cgi-multiangle-10.jpg', 'cgi-multiangle-11.jpg',
      'stock-emotions-a-1.jpg', 'stock-emotions-a-2.jpg', 'stock-emotions-a-3.jpg', 'stock-emotions-a-4.jpg', 'stock-emotions-a-5.jpg', 'stock-emotions-a-6.jpg', 'stock-emotions-a-7.jpg', 'stock-emotions-a-8.jpg',
      'stock-emotions-b-1.jpg', 'stock-emotions-b-2.jpg', 'stock-emotions-b-3.jpg', 'stock-emotions-b-4.jpg', 'stock-emotions-b-5.jpg', 'stock-emotions-b-6.jpg', 'stock-emotions-b-7.jpg', 'stock-emotions-b-8.jpg',
    ];
    // add prefix for gitpages
-    images = images.map((a) => `/human/samples/in/${a}`);
+    images = images.map((a) => `../../samples/in/${a}`);
    log('Adding static image list:', images);
  } else {
    log('Discovered images:', images);
@ -242,7 +237,7 @@ async function main() {
  // images = ['/samples/in/person-lexi.jpg', '/samples/in/person-carolina.jpg', '/samples/in/solvay1927.jpg'];
  const t0 = human.now();
-  for (let i = 0; i < images.length; i++) await AddImageElement(i, images[i], images.length);
+  for (let i = 0; i < images.length; i++) await addImageElement(i, images[i], images.length);
  const t1 = human.now();
  // print stats
@ -256,7 +251,7 @@ async function main() {
  title('');
  log('Ready');
  human.validate(userConfig);
-  human.similarity([], []);
+  human.match.similarity([], []);
 }
 window.onload = main;
--- a/demo/facematch/node-match-worker.js
+++ b/demo/facematch/node-match-worker.js
@ -1,3 +1,8 @@
 /**
 * Runs in a worker thread started by `node-match` demo app
 *
 */
 const threads = require('worker_threads');
 let debug = false;
@ -33,7 +38,8 @@ function match(descBuffer, options = { order: 2, multiplier: 20 }) {
    if (best < threshold || best === 0) break; // short circuit
  }
  best = (options.order === 2) ? Math.sqrt(best) : best ** (1 / options.order);
-  return { index, distance: best, similarity: Math.max(0, 100 - best) / 100.0 };
+  const similarity = Math.round(100 * Math.max(0, 100 - best) / 100.0) / 100;
  return { index, distance: best, similarity };
 }
 threads.parentPort?.on('message', (msg) => {
@ -47,7 +53,7 @@ threads.parentPort?.on('message', (msg) => {
  if (msg instanceof SharedArrayBuffer) { // called only once to receive reference to shared array buffer
    buffer = msg;
    view = new Float32Array(buffer); // initialize f64 view into buffer
-    if (debug) threads.parentPort?.postMessage(`buffer: ${buffer?.byteLength}`);
+    if (debug) threads.parentPort?.postMessage(`buffer: ${buffer.byteLength}`);
  }
  if (typeof msg.records !== 'undefined') { // recived every time when number of records changes
    records = msg.records;
@ -55,15 +61,15 @@ threads.parentPort?.on('message', (msg) => {
  }
  if (typeof msg.debug !== 'undefined') { // set verbose logging
    debug = msg.debug;
-    if (debug) threads.parentPort?.postMessage(`debug: ${debug}`);
+    // if (debug) threads.parentPort?.postMessage(`debug: ${debug}`);
  }
  if (typeof msg.threshold !== 'undefined') { // set minimum similarity threshold
    threshold = msg.threshold;
-    if (debug) threads.parentPort?.postMessage(`threshold: ${threshold}`);
+    // if (debug) threads.parentPort?.postMessage(`threshold: ${threshold}`);
  }
  if (typeof msg.shutdown !== 'undefined') { // got message to close worker
    if (debug) threads.parentPort?.postMessage('shutting down');
-    process.exit(0);
+    process.exit(0); // eslint-disable-line no-process-exit
  }
 });
--- a/demo/facematch/node-match.js
+++ b/demo/facematch/node-match.js
@ -1,7 +1,13 @@
 /**
 * Human demo app for NodeJS that generates random facial descriptors
 * and uses NodeJS multi-threading to start multiple threads for face matching
 * uses `node-match-worker.js` to perform actual face matching analysis
 */
 const fs = require('fs');
 const path = require('path');
 const log = require('@vladmandic/pilogger');
 const threads = require('worker_threads');
 const log = require('@vladmandic/pilogger'); // eslint-disable-line node/no-unpublished-require
 // global optinos
 const options = {
@ -9,7 +15,7 @@ const options = {
  dbMax: 10000, // maximum number of records to hold in memory
  threadPoolSize: 12, // number of worker threads to create in thread pool
  workerSrc: './node-match-worker.js', // code that executes in the worker thread
-  debug: false, // verbose messages
+  debug: true, // verbose messages
  minThreshold: 0.5, // match returns first record that meets the similarity threshold, set to 0 to always scan all records
  descLength: 1024, // descriptor length
 };
@ -154,7 +160,7 @@ async function createBuffer() {
  data.buffer = new SharedArrayBuffer(4 * options.dbMax * options.descLength); // preallocate max number of records as sharedarraybuffers cannot grow
  data.view = new Float32Array(data.buffer); // create view into buffer
  data.labels.length = 0;
-  log.data('created shared buffer:', { maxDescriptors: (data.view?.length || 0) / options.descLength, totalBytes: data.buffer.byteLength, totalElements: data.view?.length });
+  log.data('created shared buffer:', { maxDescriptors: (data.view.length || 0) / options.descLength, totalBytes: data.buffer.byteLength, totalElements: data.view.length });
 }
 async function main() {
@ -170,7 +176,7 @@ async function main() {
    data.requestID++; // increase request id
    if (testOptions.fuzDescriptors) match(fuzDescriptor(descriptor)); // fuz descriptor for harder match
    else match(descriptor);
-    if (options.debug) log.info('submited job', data.requestID); // we already know what we're searching for so we can compare results
+    if (options.debug) log.debug('submited job', data.requestID); // we already know what we're searching for so we can compare results
  }
  log.state('submitted:', { matchJobs: testOptions.maxJobs, poolSize: data.workers.length, activeWorkers: data.workers.filter((worker) => !!worker).length });
 }
--- a/demo/helpers/gl-bench.js
+++ b/demo/helpers/gl-bench.js
@ -1,4 +1,3 @@
 // @ts-nocheck
 // based on: https://github.com/munrocket/gl-bench
 const UICSS = `
@ -43,9 +42,7 @@ class GLBench {
  constructor(gl, settings = {}) {
    this.css = UICSS;
    this.svg = UISVG;
    // eslint-disable-next-line @typescript-eslint/no-empty-function
    this.paramLogger = () => {};
    // eslint-disable-next-line @typescript-eslint/no-empty-function
    this.chartLogger = () => {};
    this.chartLen = 20;
    this.chartHz = 20;
@ -92,7 +89,6 @@ class GLBench {
      const addProfiler = (fn, self, target) => {
        const t = self.now();
        // eslint-disable-next-line prefer-rest-params
        fn.apply(target, arguments);
        if (self.trackGPU) self.finished.push(glFinish(t, self.activeAccums.slice(0)));
      };
@ -107,13 +103,11 @@ class GLBench {
      if (gl[fn]) {
        gl[fn] = addProfiler(gl[fn], this, gl);
      } else {
        // eslint-disable-next-line no-console
        console.log('bench: cannot attach to webgl function');
      }
      /*
      gl.getExtension = ((fn, self) => {
        // eslint-disable-next-line prefer-rest-params
        const ext = fn.apply(gl, arguments);
        if (ext) {
          ['drawElementsInstancedANGLE', 'drawBuffersWEBGL'].forEach((fn2) => {
@ -148,7 +142,6 @@ class GLBench {
        return (i, cpu, gpu, mem, fps, totalTime, frameId) => {
          nodes['gl-cpu'][i].style.strokeDasharray = (cpu * 0.27).toFixed(0) + ' 100';
          nodes['gl-gpu'][i].style.strokeDasharray = (gpu * 0.27).toFixed(0) + ' 100';
          // eslint-disable-next-line no-nested-ternary
          nodes['gl-mem'][i].innerHTML = names[i] ? names[i] : (mem ? 'mem: ' + mem.toFixed(0) + 'mb' : '');
          nodes['gl-fps'][i].innerHTML = 'FPS: ' + fps.toFixed(1);
          logger(names[i], cpu, gpu, mem, fps, totalTime, frameId);
--- a/demo/helpers/jsonview.js
+++ b/demo/helpers/jsonview.js
@ -64,9 +64,7 @@ function createNode() {
    hideChildren() {
      if (Array.isArray(this.children)) {
        this.children.forEach((item) => {
          // @ts-ignore
          item['elem']['classList'].add('hide');
          // @ts-ignore
          if (item['expanded']) item.hideChildren();
        });
      }
@ -74,9 +72,7 @@ function createNode() {
    showChildren() {
      if (Array.isArray(this.children)) {
        this.children.forEach((item) => {
          // @ts-ignore
          item['elem']['classList'].remove('hide');
          // @ts-ignore
          if (item['expanded']) item.showChildren();
        });
      }
--- a/demo/helpers/menu.js
+++ b/demo/helpers/menu.js
@ -119,7 +119,6 @@ class Menu {
    this.menu.appendChild(this.container);
    if (typeof parent === 'object') parent.appendChild(this.menu);
    // @ts-ignore undefined
    else document.getElementById(parent).appendChild(this.menu);
  }
@ -184,7 +183,6 @@ class Menu {
      this.hidden = !this.hidden;
      const all = document.getElementsByClassName('menu');
      for (const item of all) {
        // @ts-ignore
        item.style.display = this.hidden ? 'none' : 'block';
      }
    });
@ -241,7 +239,6 @@ class Menu {
    el.addEventListener('change', (evt) => {
      if (evt.target) {
        object[variable] = parseInt(evt.target['value']) === parseFloat(evt.target['value']) ? parseInt(evt.target['value']) : parseFloat(evt.target['value']);
        // @ts-ignore
        evt.target.setAttribute('value', evt.target['value']);
        if (callback) callback(evt.target['value']);
      }
@ -286,7 +283,6 @@ class Menu {
    return el;
  }
  // eslint-disable-next-line class-methods-use-this
  updateValue(title, val, suffix = '') {
    const el = document.getElementById(`menu-val-${title}`);
    if (el) el.innerText = `${title}: ${val}${suffix}`;
@ -303,11 +299,9 @@ class Menu {
    return el;
  }
  // eslint-disable-next-line class-methods-use-this
  async updateChart(id, values) {
    if (!values || (values.length === 0)) return;
    /** @type {HTMLCanvasElement} */ 
    // @ts-ignore undefined
    const canvas = document.getElementById(`menu-canvas-${id}`);
    if (!canvas) return;
    const ctx = canvas.getContext('2d');
--- a/demo/helpers/webrtc.js
+++ b/demo/helpers/webrtc.js
@ -4,8 +4,7 @@ async function log(...msg) {
  if (debug) {
    const dt = new Date();
    const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;
-    // eslint-disable-next-line no-console
+    console.log(ts, 'webrtc', ...msg); // eslint-disable-line no-console
    console.log(ts, 'webrtc', ...msg);
  }
 }
--- a/demo/index-pwa.js
+++ b/demo/index-pwa.js
@ -2,6 +2,7 @@
 * PWA Service Worker for Human main demo
 */
 /* eslint-disable no-restricted-globals */
 /// <reference lib="webworker" />
 const skipCaching = false;
@ -19,8 +20,7 @@ const stats = { hit: 0, miss: 0 };
 const log = (...msg) => {
  const dt = new Date();
  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;
-  // eslint-disable-next-line no-console
+  console.log(ts, 'pwa', ...msg); // eslint-disable-line no-console
  console.log(ts, 'pwa', ...msg);
 };
 async function updateCached(req) {
@ -31,7 +31,7 @@ async function updateCached(req) {
        caches
          .open(cacheName)
          .then((cache) => cache.put(req, update))
-          .catch((err) => log('cache update error', err));
+          .catch((err) => log('cache update error', err)); // eslint-disable-line promise/no-nesting
      }
      return true;
    })
@ -75,14 +75,13 @@ async function getCached(evt) {
 }
 function cacheInit() {
  // eslint-disable-next-line promise/catch-or-return
  caches.open(cacheName)
    // eslint-disable-next-line promise/no-nesting
    .then((cache) => cache.addAll(cacheFiles)
-      .then(
+      .then( // eslint-disable-line promise/no-nesting
        () => log('cache refresh:', cacheFiles.length, 'files'),
        (err) => log('cache error', err),
-      ));
+      ))
    .catch(() => log('cache error'));
 }
 if (!listening) {
@ -99,14 +98,12 @@ if (!listening) {
  self.addEventListener('install', (evt) => {
    log('install');
    // @ts-ignore scope for self is ServiceWorkerGlobalScope not Window
    self.skipWaiting();
    evt.waitUntil(cacheInit);
  });
  self.addEventListener('activate', (evt) => {
    log('activate');
    // @ts-ignore scope for self is ServiceWorkerGlobalScope not Window
    evt.waitUntil(self.clients.claim());
  });
@ -114,7 +111,7 @@ if (!listening) {
    const uri = new URL(evt.request.url);
    // if (uri.pathname === '/') { log('cache skip /', evt.request); return; } // skip root access requests
    if (evt.request.cache === 'only-if-cached' && evt.request.mode !== 'same-origin') return; // required due to chrome bug
-    if (uri.origin !== location.origin) return; // skip non-local requests
+    if (uri.origin !== self.location.origin) return; // skip non-local requests
    if (evt.request.method !== 'GET') return; // only cache get requests
    if (evt.request.url.includes('/api/')) return; // don't cache api requests, failures are handled at the time of call
@ -129,7 +126,7 @@ if (!listening) {
    log(`PWA: ${evt.type}`);
    if (refreshed) return;
    refreshed = true;
-    location.reload();
+    self.location.reload();
  });
  listening = true;
--- a/demo/index-worker.js
+++ b/demo/index-worker.js
@ -6,10 +6,9 @@
 /// <reference lib="webworker"/>
 // load Human using IIFE script as Chome Mobile does not support Modules as Workers
-self.importScripts('../dist/human.js');
+self.importScripts('../dist/human.js'); // eslint-disable-line no-restricted-globals
 let busy = false;
 // @ts-ignore
 // eslint-disable-next-line new-cap, no-undef
 const human = new Human.default();
--- a/demo/index.html
+++ b/demo/index.html
@ -35,7 +35,7 @@
      .video { display: none; }
      .canvas { margin: 0 auto; }
      .bench { position: absolute; right: 0; bottom: 0; }
-      .compare-image { width: 256px; position: absolute; top: 150px; left: 30px; box-shadow: 0 0 2px 2px black; background: black; display: none; }
+      .compare-image { width: 200px; position: absolute; top: 150px; left: 30px; box-shadow: 0 0 2px 2px black; background: black; display: none; }
      .loader { width: 300px; height: 300px; border: 3px solid transparent; border-radius: 50%; border-top: 4px solid #f15e41; animation: spin 4s linear infinite; position: absolute; bottom: 15%; left: 50%; margin-left: -150px; z-index: 15; }
      .loader::before, .loader::after { content: ""; position: absolute; top: 6px; bottom: 6px; left: 6px; right: 6px; border-radius: 50%; border: 4px solid transparent; }
      .loader::before { border-top-color: #bad375; animation: 3s spin linear infinite; }
@ -89,9 +89,9 @@
  <body>
    <div id="play" class="play icon-play"></div>
    <div id="background">
-      <div class='wave one'></div>
+      <div class="wave one"></div>
-      <div class='wave two'></div>
+      <div class="wave two"></div>
-      <div class='wave three'></div>         
+      <div class="wave three"></div>         
    </div>
    <div id="loader" class="loader"></div>
    <div id="status" class="status"></div>
@ -107,13 +107,9 @@
      <video id="video" playsinline class="video"></video>
    </div>
    <div id="compare-container" class="compare-image">
-      <canvas id="compare-canvas" width="256" height="256"></canvas>
+      <canvas id="compare-canvas" width="200" height="200"></canvas>
      <div id="similarity"></div>
    </div>
    <div id="segmentation-container" class="compare-image">
      <canvas id="segmentation-mask" width="256" height="256" style="width: 256px; height: 256px;"></canvas>
      <canvas id="segmentation-canvas" width="256" height="256" style="width: 256px; height: 256px;"></canvas>
    </div>
    <div id="samples-container" class="samples-container"></div>
    <div id="hint" class="hint"></div>
    <div id="log" class="log"></div>
--- a/demo/index.js
+++ b/demo/index.js
@ -18,11 +18,12 @@
 * ui={}: contains all variables exposed in the UI
 */
-// test url <https://human.local/?worker=false&async=false&bench=false&draw=true&warmup=full&backend=humangl>
+// WARNING!!!
 // This demo is written using older code style and a lot of manual setup
 // Newer versions of Human have richer functionality allowing for much cleaner & easier usage
 // It is recommended to use other demos such as `demo/typescript` for usage examples
-// @ts-nocheck // typescript checks disabled as this is pure javascript
+import { Human } from '../dist/human.esm.js'; // equivalent of @vladmandic/human
 import Human from '../dist/human.esm.js'; // equivalent of @vladmandic/human
 import Menu from './helpers/menu.js';
 import GLBench from './helpers/gl-bench.js';
 import webRTC from './helpers/webrtc.js';
@ -36,7 +37,7 @@ let userConfig = {
  // hand: { enabled: false },
  /*
  warmup: 'none',
-  backend: 'humangl',
+  backend: 'webgl',
  debug: true,
  wasmPath: 'https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@3.9.0/dist/',
  async: false,
@ -56,6 +57,12 @@ let userConfig = {
  // body: { enabled: true, modelPath: 'movenet-multipose.json' },
  segmentation: { enabled: false },
  */
  /*
  face: { iris: { enabled: false }, emotion: { enabled: false } },
  hand: { enabled: false },
  body: { enabled: false },
  gesture: { enabled: false },
  */
 };
 const drawOptions = {
@ -79,7 +86,7 @@ const ui = {
  facing: true, // camera facing front or back
  baseBackground: 'rgba(50, 50, 50, 1)', // 'grey'
  columns: 2, // when processing sample images create this many columns
-  useWorker: true, // use web workers for processing
+  useWorker: false, // use web workers for processing
  worker: 'index-worker.js',
  maxFPSframes: 10, // keep fps history for how many frames
  modelsPreload: false, // preload human models on startup
@ -107,7 +114,6 @@ const ui = {
  results: false, // show results tree
  lastFrame: 0, // time of last frame processing
  viewportSet: false, // internal, has custom viewport been set
  background: null, // holds instance of segmentation background image
  transferCanvas: null, // canvas used to transfer data to and from worker
  // webrtc
@ -147,7 +153,7 @@ let bench;
 let lastDetectedResult = {};
 // helper function: async pause
-// eslint-disable-next-line @typescript-eslint/no-unused-vars, no-unused-vars
+// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars
 const delay = (ms) => new Promise((resolve) => { setTimeout(resolve, ms); });
 // helper function: translates json to human readable string
@ -165,8 +171,7 @@ function str(...msg) {
 function log(...msg) {
  const dt = new Date();
  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;
-  // eslint-disable-next-line no-console
+  if (ui.console) console.log(ts, ...msg); // eslint-disable-line no-console
  if (ui.console) console.log(ts, ...msg);
 }
 let prevStatus = '';
@ -180,17 +185,16 @@ function status(msg) {
    prevStatus = msg;
  } else {
    const video = document.getElementById('video');
-    const playing = (video.srcObject !== null) && !video.paused;
+    const playing = isLive(video) && !video.paused; // eslint-disable-line no-use-before-define
    document.getElementById('play').style.display = playing ? 'none' : 'block';
    document.getElementById('loader').style.display = 'none';
    div.innerText = '';
  }
 }
-async function videoPlay() {
+async function videoPlay(videoElement = document.getElementById('video')) {
  document.getElementById('btnStartText').innerHTML = 'pause video';
-  await document.getElementById('video').play();
+  await videoElement.play();
  // status();
 }
 async function videoPause() {
@ -203,29 +207,28 @@ async function videoPause() {
 const compare = { enabled: false, original: null };
 async function calcSimmilarity(result) {
  document.getElementById('compare-container').onclick = () => {
    log('resetting face compare baseline:');
    compare.original = null;
  };
  document.getElementById('compare-container').style.display = compare.enabled ? 'block' : 'none';
-  if (!compare.enabled) return;
+  if (!compare.enabled) {
    compare.original = null;
    return;
  }
  if (!result || !result.face || !result.face[0] || !result.face[0].embedding) return;
  if (!(result.face.length > 0) || (result.face[0].embedding.length <= 64)) return;
  if (!compare.original) {
    compare.original = result;
    log('setting face compare baseline:', result.face[0]);
    if (result.face[0].tensor) {
      const enhanced = human.enhance(result.face[0]);
      if (enhanced) {
      const c = document.getElementById('orig');
-        const squeeze = human.tf.squeeze(enhanced);
+      human.draw.tensor(result.face[0].tensor, c);
        const norm = human.tf.div(squeeze, 255);
        human.tf.browser.toPixels(norm, c);
        human.tf.dispose(enhanced);
        human.tf.dispose(squeeze);
        human.tf.dispose(norm);
      }
    } else {
      document.getElementById('compare-canvas').getContext('2d').drawImage(compare.original.canvas, 0, 0, 200, 200);
    }
  }
-  const similarity = human.similarity(compare.original.face[0].embedding, result.face[0].embedding);
+  const similarity = human.match.similarity(compare.original.face[0].embedding, result.face[0].embedding);
  document.getElementById('similarity').innerText = `similarity: ${Math.trunc(1000 * similarity) / 10}%`;
 }
@ -253,21 +256,7 @@ async function drawResults(input) {
  // draw fps chart
  await menu.process.updateChart('FPS', ui.detectFPS);
-  document.getElementById('segmentation-container').style.display = userConfig.segmentation.enabled ? 'block' : 'none';
+  if (!result.canvas || ui.buffered) { // refresh with input if using buffered output or if missing canvas
  if (userConfig.segmentation.enabled && ui.buffered) { // refresh segmentation if using buffered output
    const seg = await human.segmentation(input, ui.background);
    if (seg.alpha) {
      const canvasSegMask = document.getElementById('segmentation-mask');
      const ctxSegMask = canvasSegMask.getContext('2d');
      ctxSegMask.clearRect(0, 0, canvasSegMask.width, canvasSegMask.height); // need to clear as seg.alpha is alpha based canvas so it adds
      ctxSegMask.drawImage(seg.alpha, 0, 0, seg.alpha.width, seg.alpha.height, 0, 0, canvasSegMask.width, canvasSegMask.height);
      const canvasSegCanvas = document.getElementById('segmentation-canvas');
      const ctxSegCanvas = canvasSegCanvas.getContext('2d');
      ctxSegCanvas.clearRect(0, 0, canvasSegCanvas.width, canvasSegCanvas.height); // need to clear as seg.alpha is alpha based canvas so it adds
      ctxSegCanvas.drawImage(seg.canvas, 0, 0, seg.alpha.width, seg.alpha.height, 0, 0, canvasSegCanvas.width, canvasSegCanvas.height);
    }
    // result.canvas = seg.alpha;
  } else if (!result.canvas || ui.buffered) { // refresh with input if using buffered output or if missing canvas
    const image = await human.image(input, false);
    result.canvas = image.canvas;
    human.tf.dispose(image.tensor);
@ -336,14 +325,12 @@ async function drawResults(input) {
      videoPause();
      ui.drawThread = null;
    }
-  } else {
+  } else if (ui.drawThread) {
    if (ui.drawThread) {
    log('stopping buffered refresh');
    cancelAnimationFrame(ui.drawThread);
    ui.drawThread = null;
  }
 }
 }
 // setup webcam
 let initialCameraAccess = true;
@ -432,8 +419,7 @@ async function setupCamera() {
  ui.menuWidth.input.setAttribute('value', video.videoWidth);
  ui.menuHeight.input.setAttribute('value', video.videoHeight);
  if (live || ui.autoPlay) await videoPlay();
-  // eslint-disable-next-line no-use-before-define
+  if ((live || ui.autoPlay) && !ui.detectThread) runHumanDetect(video, canvas); // eslint-disable-line no-use-before-define
  if ((live || ui.autoPlay) && !ui.detectThread) runHumanDetect(video, canvas);
  return 'camera stream ready';
 }
@ -487,8 +473,7 @@ function webWorker(input, image, canvas, timestamp) {
      ui.framesDetect++;
      if (!ui.drawThread) drawResults(input);
      if (isLive(input)) {
-        // eslint-disable-next-line no-use-before-define
+        ui.detectThread = requestAnimationFrame((now) => runHumanDetect(input, canvas, now)); // eslint-disable-line no-use-before-define
        ui.detectThread = requestAnimationFrame((now) => runHumanDetect(input, canvas, now));
      }
    });
  }
@ -525,19 +510,9 @@ function runHumanDetect(input, canvas, timestamp) {
    // perform detection in worker
    webWorker(input, data, canvas, timestamp);
  } else {
-    human.detect(input, userConfig).then((result) => {
+    human.detect(input, userConfig)
      .then((result) => {
        status();
      /*
      setTimeout(async () => { // simulate gl context lost 2sec after initial detection
        const ext = human.gl && human.gl.gl ? human.gl.gl.getExtension('WEBGL_lose_context') : {};
        if (ext && ext.loseContext) {
          log('simulate context lost:', human.env.webgl, human.gl, ext);
          human.gl.gl.getExtension('WEBGL_lose_context').loseContext();
          await videoPause();
          status('Exception: WebGL');
        }
      }, 2000);
      */
        if (result.performance && result.performance.total) ui.detectFPS.push(1000 / result.performance.total);
        if (ui.detectFPS.length > ui.maxFPSframes) ui.detectFPS.shift();
        if (ui.bench) {
@ -554,7 +529,9 @@ function runHumanDetect(input, canvas, timestamp) {
          ui.framesDetect++;
          ui.detectThread = requestAnimationFrame((now) => runHumanDetect(input, canvas, now));
        }
-    });
+        return result;
      })
      .catch(() => log('human detect error'));
  }
 }
@ -601,8 +578,7 @@ async function processImage(input, title) {
        // copy to clipboard on click
        if (typeof ClipboardItem !== 'undefined' && navigator.clipboard) {
          evt.target.toBlob((blob) => {
-            // eslint-disable-next-line no-undef
+            const item = new ClipboardItem({ 'image/png': blob }); // eslint-disable-line no-undef
            const item = new ClipboardItem({ 'image/png': blob });
            navigator.clipboard.write([item]);
            log('copied image to clipboard');
          });
@ -630,20 +606,17 @@ async function processImage(input, title) {
 async function processVideo(input, title) {
  status(`processing video: ${title}`);
-  const video = document.createElement('video');
+  const video = document.getElementById('video');
  const canvas = document.getElementById('canvas');
  video.id = 'video-file';
  video.controls = true;
  video.loop = true;
  // video.onerror = async () => status(`video loading error: ${video.error.message}`);
  video.addEventListener('error', () => status(`video loading error: ${video.error.message}`));
  video.addEventListener('canplay', async () => {
    for (const m of Object.values(menu)) m.hide();
    document.getElementById('samples-container').style.display = 'none';
    canvas.style.display = 'block';
    await videoPlay();
-    if (!ui.detectThread) runHumanDetect(video, canvas);
+    runHumanDetect(video, canvas);
  });
  video.srcObject = null;
  video.src = input;
 }
@ -654,9 +627,8 @@ async function detectVideo() {
  const canvas = document.getElementById('canvas');
  canvas.style.display = 'block';
  cancelAnimationFrame(ui.detectThread);
-  if ((video.srcObject !== null) && !video.paused) {
+  if (isLive(video) && !video.paused) {
    await videoPause();
    // if (ui.drawThread) cancelAnimationFrame(ui.drawThread);
  } else {
    const cameraError = await setupCamera();
    if (!cameraError) {
@ -732,7 +704,6 @@ function setupMenu() {
  menu.image.addBool('technicolor', userConfig.filter, 'technicolor', (val) => userConfig.filter.technicolor = val);
  menu.image.addBool('polaroid', userConfig.filter, 'polaroid', (val) => userConfig.filter.polaroid = val);
  menu.image.addHTML('<input type="file" id="file-input" class="input-file"></input> &nbsp input');
  menu.image.addHTML('<input type="file" id="file-background" class="input-file"></input> &nbsp background');
  menu.process = new Menu(document.body, '', { top, left: x[2] });
  menu.process.addList('backend', ['cpu', 'webgl', 'wasm', 'humangl'], userConfig.backend, (val) => userConfig.backend = val);
@ -780,8 +751,6 @@ function setupMenu() {
  menu.models.addHTML('<hr style="border-style: inset; border-color: dimgray">');
  menu.models.addBool('gestures', userConfig.gesture, 'enabled', (val) => userConfig.gesture.enabled = val);
  menu.models.addHTML('<hr style="border-style: inset; border-color: dimgray">');
  menu.models.addBool('body segmentation', userConfig.segmentation, 'enabled', (val) => userConfig.segmentation.enabled = val);
  menu.models.addHTML('<hr style="border-style: inset; border-color: dimgray">');
  menu.models.addBool('object detection', userConfig.object, 'enabled', (val) => userConfig.object.enabled = val);
  menu.models.addHTML('<hr style="border-style: inset; border-color: dimgray">');
  menu.models.addBool('face compare', compare, 'enabled', (val) => {
@ -801,6 +770,7 @@ function setupMenu() {
 async function resize() {
  window.onresize = null;
  log('resize');
  // best setting for mobile, ignored for desktop
  // can set dynamic value such as Math.min(1, Math.round(100 * window.innerWidth / 960) / 100);
  const viewportScale = 0.7;
@ -849,42 +819,12 @@ async function processDataURL(f, action) {
        if (e.target.result.startsWith('data:video')) await processVideo(e.target.result, f.name);
        document.getElementById('canvas').style.display = 'none';
      }
      if (action === 'background') {
        const image = new Image();
        image.onerror = async () => status('image loading error');
        image.onload = async () => {
          ui.background = image;
          if (document.getElementById('canvas').style.display === 'block') { // replace canvas used for video
            const canvas = document.getElementById('canvas');
            const ctx = canvas.getContext('2d');
            const seg = await human.segmentation(canvas, ui.background, userConfig);
            if (seg.canvas) ctx.drawImage(seg.canvas, 0, 0);
          } else {
            const canvases = document.getElementById('samples-container').children; // replace loaded images
            for (const canvas of canvases) {
              const ctx = canvas.getContext('2d');
              const seg = await human.segmentation(canvas, ui.background, userConfig);
              if (seg.canvas) ctx.drawImage(seg.canvas, 0, 0);
            }
          }
        };
        image.src = e.target.result;
      }
      resolve(true);
    };
    reader.readAsDataURL(f);
  });
 }
 async function runSegmentation() {
  document.getElementById('file-background').onchange = async (evt) => {
    userConfig.segmentation.enabled = true;
    evt.preventDefault();
    if (evt.target.files.length < 2) ui.columns = 1;
    for (const f of evt.target.files) await processDataURL(f, 'background');
  };
 }
 async function dragAndDrop() {
  document.body.addEventListener('dragenter', (evt) => evt.preventDefault());
  document.body.addEventListener('dragleave', (evt) => evt.preventDefault());
@ -922,10 +862,10 @@ async function pwaRegister() {
      const regs = await navigator.serviceWorker.getRegistrations();
      for (const reg of regs) {
        log('pwa found:', reg.scope);
-        if (reg.scope.startsWith(location.origin)) found = reg;
+        if (reg.scope.startsWith(window.location.origin)) found = reg;
      }
      if (!found) {
-        const reg = await navigator.serviceWorker.register(pwa.scriptFile, { scope: location.pathname });
+        const reg = await navigator.serviceWorker.register(pwa.scriptFile, { scope: window.location.pathname });
        found = reg;
        log('pwa registered:', reg.scope);
      }
@ -957,8 +897,7 @@ async function main() {
      if (ui.detectThread) cancelAnimationFrame(ui.detectThread);
      if (ui.drawThread) cancelAnimationFrame(ui.drawThread);
      const msg = evt.reason.message || evt.reason || evt;
-      // eslint-disable-next-line no-console
+      console.error(msg); // eslint-disable-line no-console
      console.error(msg);
      document.getElementById('log').innerHTML = msg;
      status(`exception: ${msg}`);
      evt.preventDefault();
@ -981,7 +920,7 @@ async function main() {
  await pwaRegister();
  // parse url search params
-  const params = new URLSearchParams(location.search);
+  const params = new URLSearchParams(window.location.search);
  log('url options:', params.toString());
  if (params.has('worker')) {
    ui.useWorker = JSON.parse(params.get('worker'));
@ -1024,10 +963,8 @@ async function main() {
  // we've merged human defaults with user config and now lets store it back so it can be accessed by methods such as menu
  userConfig = human.config;
  if (typeof tf !== 'undefined') {
-    // eslint-disable-next-line no-undef
+    log('TensorFlow external version:', tf.version); // eslint-disable-line no-undef
-    log('TensorFlow external version:', tf.version);
+    human.tf = tf; // eslint-disable-line no-undef
    // eslint-disable-next-line no-undef
    human.tf = tf; // use externally loaded version of tfjs
  }
  log('tfjs version:', human.tf.version.tfjs);
@ -1040,8 +977,7 @@ async function main() {
  if (ui.modelsPreload && !ui.useWorker) {
    status('loading');
    await human.load(userConfig); // this is not required, just pre-loads all models
-    const loaded = Object.keys(human.models).filter((a) => human.models[a]);
+    log('demo loaded models:', human.models.loaded());
    log('demo loaded models:', loaded);
  } else {
    await human.init();
  }
@ -1063,9 +999,6 @@ async function main() {
  // init drag & drop
  await dragAndDrop();
  // init segmentation
  await runSegmentation();
  if (params.has('image')) {
    try {
      const image = JSON.parse(params.get('image'));
@ -1086,7 +1019,7 @@ async function main() {
  }
  if (human.config.debug) log('environment:', human.env);
-  if (human.config.backend === 'humangl' && human.config.debug) log('backend:', human.gl);
+  if (human.config.backend === 'webgl' && human.config.debug) log('backend:', human.gl);
 }
 window.onload = main;
--- a/demo/multithread/README.md
+++ b/demo/multithread/README.md
@ -16,10 +16,11 @@
 node demo/nodejs/node-multiprocess.js
 ```
 <!-- eslint-skip -->
 ```json
 2021-06-01 08:54:19 INFO:  @vladmandic/human version 2.0.0
 2021-06-01 08:54:19 INFO:  User: vlado Platform: linux Arch: x64 Node: v16.0.0
-2021-06-01 08:54:19 INFO:  FaceAPI multi-process test
+2021-06-01 08:54:19 INFO:  Human multi-process test
 2021-06-01 08:54:19 STATE: Enumerated images: ./assets 15
 2021-06-01 08:54:19 STATE: Main: started worker: 130362
 2021-06-01 08:54:19 STATE: Main: started worker: 130363
--- a/demo/multithread/index.html
+++ b/demo/multithread/index.html
@ -9,10 +9,10 @@
    <meta name="description" content="Human: 3D Face Detection, Body Pose, Hand & Finger Tracking, Iris Tracking, Age & Gender Prediction, Emotion Prediction & Gesture Recognition; Author: Vladimir Mandic <https://github.com/vladmandic>">
    <meta name="msapplication-tooltip" content="Human: 3D Face Detection, Body Pose, Hand & Finger Tracking, Iris Tracking, Age & Gender Prediction, Emotion Prediction & Gesture Recognition; Author: Vladimir Mandic <https://github.com/vladmandic>">
    <meta name="theme-color" content="#000000">
-    <link rel="manifest" href="../manifest.webmanifest">
+    <link rel="manifest" href="../../manifest.webmanifest">
    <link rel="shortcut icon" href="../../favicon.ico" type="image/x-icon">
    <link rel="apple-touch-icon" href="../../assets/icon.png">
-    <script src="./index.js" type="module"></script>
+    <script src="../multithread/index.js" type="module"></script>
    <style>
      @font-face { font-family: 'Lato'; font-display: swap; font-style: normal; font-weight: 100; src: local('Lato'), url('../../assets/lato-light.woff2') }
      html { font-family: 'Lato', 'Segoe UI'; font-size: 16px; font-variant: small-caps; }
--- a/demo/multithread/index.js
+++ b/demo/multithread/index.js
@ -5,15 +5,15 @@
 *
 */
-import Human from '../../dist/human.esm.js'; // equivalent of @vladmandic/human
+import { Human } from '../../dist/human.esm.js'; // equivalent of @vladmandic/human
 import GLBench from '../helpers/gl-bench.js';
-const workerJS = './worker.js';
+const workerJS = '../multithread/worker.js';
 const config = {
  main: { // processes input and runs gesture analysis
    warmup: 'none',
-    backend: 'humangl',
+    backend: 'webgl',
    modelBasePath: '../../models/',
    async: false,
    filter: { enabled: true },
@ -26,7 +26,7 @@ const config = {
  },
  face: { // runs all face models
    warmup: 'none',
-    backend: 'humangl',
+    backend: 'webgl',
    modelBasePath: '../../models/',
    async: false,
    filter: { enabled: false },
@ -39,7 +39,7 @@ const config = {
  },
  body: { // runs body model
    warmup: 'none',
-    backend: 'humangl',
+    backend: 'webgl',
    modelBasePath: '../../models/',
    async: false,
    filter: { enabled: false },
@ -52,7 +52,7 @@ const config = {
  },
  hand: { // runs hands model
    warmup: 'none',
-    backend: 'humangl',
+    backend: 'webgl',
    modelBasePath: '../../models/',
    async: false,
    filter: { enabled: false },
@ -65,7 +65,7 @@ const config = {
  },
  object: { // runs object model
    warmup: 'none',
-    backend: 'humangl',
+    backend: 'webgl',
    modelBasePath: '../../models/',
    async: false,
    filter: { enabled: false },
@ -130,8 +130,7 @@ const result = { // initialize empty result object which will be partially fille
 function log(...msg) {
  const dt = new Date();
  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;
-  // eslint-disable-next-line no-console
+  console.log(ts, ...msg); // eslint-disable-line no-console
  console.log(ts, ...msg);
 }
 async function drawResults() {
@ -203,7 +202,9 @@ async function setupCamera() {
    },
  };
  // enumerate devices for diag purposes
-  navigator.mediaDevices.enumerateDevices().then((devices) => log('enumerated devices:', devices));
+  navigator.mediaDevices.enumerateDevices()
    .then((devices) => log('enumerated devices:', devices))
    .catch(() => log('mediaDevices error'));
  log('camera constraints', constraints);
  try {
    stream = await navigator.mediaDevices.getUserMedia(constraints);
@ -230,7 +231,7 @@ async function setupCamera() {
    };
  });
  // attach input to video element
-  if (stream && video) video['srcObject'] = stream;
+  if (stream && video) video.srcObject = stream;
  return promise;
 }
--- a/demo/multithread/node-multiprocess-worker.js
+++ b/demo/multithread/node-multiprocess-worker.js
@ -6,11 +6,10 @@
 */
 const fs = require('fs');
-const log = require('@vladmandic/pilogger');
+const log = require('@vladmandic/pilogger'); // eslint-disable-line node/no-unpublished-require
-// workers actual import tfjs and faceapi modules
+// workers actual import tfjs and human modules
-// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars
+const tf = require('@tensorflow/tfjs-node'); // eslint-disable-line node/no-unpublished-require
 const tf = require('@tensorflow/tfjs-node');
 const Human = require('../../dist/human.node.js').default; // or const Human = require('../dist/human.node-gpu.js').default;
 let human = null;
@ -36,7 +35,7 @@ const myConfig = {
  object: { enabled: true },
 };
-// read image from a file and create tensor to be used by faceapi
+// read image from a file and create tensor to be used by human
 // this way we don't need any monkey patches
 // you can add any pre-proocessing here such as resizing, etc.
 async function image(img) {
@ -45,7 +44,7 @@ async function image(img) {
  return tensor;
 }
-// actual faceapi detection
+// actual human detection
 async function detect(img) {
  const tensor = await image(img);
  const result = await human.detect(tensor);
@ -64,11 +63,9 @@ async function main() {
  // on worker start first initialize message handler so we don't miss any messages
  process.on('message', (msg) => {
-    // @ts-ignore
+    // if main told worker to exit
-    if (msg.exit && process.exit) process.exit(); // if main told worker to exit
+    if (msg.exit && process.exit) process.exit(); // eslint-disable-line no-process-exit
    // @ts-ignore
    if (msg.test && process.send) process.send({ test: true });
    // @ts-ignore
    if (msg.image) detect(msg.image); // if main told worker to process image
    log.data('Worker received message:', process.pid, msg); // generic log
  });
--- a/demo/multithread/node-multiprocess.js
+++ b/demo/multithread/node-multiprocess.js
@ -8,13 +8,12 @@
 const fs = require('fs');
 const path = require('path');
-// eslint-disable-next-line import/no-extraneous-dependencies, node/no-unpublished-require
+const childProcess = require('child_process'); // eslint-disable-line camelcase
-const log = require('@vladmandic/pilogger'); // this is my simple logger with few extra features
+const log = require('@vladmandic/pilogger'); // eslint-disable-line node/no-unpublished-require
 const child_process = require('child_process');
 // note that main process does not import human or tfjs at all, it's all done from worker process
-const workerFile = 'demo/nodejs/node-multiprocess-worker.js';
+const workerFile = 'demo/multithread/node-multiprocess-worker.js';
-const imgPathRoot = './assets'; // modify to include your sample images
+const imgPathRoot = './samples/in'; // modify to include your sample images
 const numWorkers = 4; // how many workers will be started
 const workers = []; // this holds worker processes
 const images = []; // this holds queue of enumerated images
@ -23,7 +22,7 @@ let numImages;
 // trigered by main when worker sends ready message
 // if image pool is empty, signal worker to exit otherwise dispatch image to worker and remove image from queue
-async function detect(worker) {
+async function submitDetect(worker) {
  if (!t[2]) t[2] = process.hrtime.bigint(); // first time do a timestamp so we can measure initial latency
  if (images.length === numImages) worker.send({ test: true }); // for first image in queue just measure latency
  if (images.length === 0) worker.send({ exit: true }); // nothing left in queue
@ -58,7 +57,7 @@ async function main() {
  });
  log.header();
-  log.info('FaceAPI multi-process test');
+  log.info('Human multi-process test');
  // enumerate all images into queue
  const dir = fs.readdirSync(imgPathRoot);
@ -74,13 +73,13 @@ async function main() {
  // manage worker processes
  for (let i = 0; i < numWorkers; i++) {
    // create worker process
-    workers[i] = await child_process.fork(workerFile, ['special']);
+    workers[i] = await childProcess.fork(workerFile, ['special']);
    // parse message that worker process sends back to main
    // if message is ready, dispatch next image in queue
    // if message is processing result, just print how many faces were detected
    // otherwise it's an unknown message
    workers[i].on('message', (msg) => {
-      if (msg.ready) detect(workers[i]);
+      if (msg.ready) submitDetect(workers[i]);
      else if (msg.image) log.data('Main: worker finished:', workers[i].pid, 'detected faces:', msg.detected.face?.length, 'bodies:', msg.detected.body?.length, 'hands:', msg.detected.hand?.length, 'objects:', msg.detected.object?.length);
      else if (msg.test) measureLatency();
      else log.data('Main: worker message:', workers[i].pid, msg);
--- a/demo/multithread/worker.js
+++ b/demo/multithread/worker.js
@ -1,7 +1,7 @@
 /// <reference lib="webworker" />
 // load Human using IIFE script as Chome Mobile does not support Modules as Workers
-self.importScripts('../../dist/human.js');
+self.importScripts('../../dist/human.js'); // eslint-disable-line no-restricted-globals
 let human;
@ -9,9 +9,8 @@ onmessage = async (msg) => {
  // received from index.js using:
  // worker.postMessage({ image: image.data.buffer, width: canvas.width, height: canvas.height, config }, [image.data.buffer]);
-  // @ts-ignore // Human is registered as global namespace using IIFE script
+  // Human is registered as global namespace using IIFE script
-  // eslint-disable-next-line no-undef, new-cap
+  if (!human) human = new Human.default(msg.data.config); // eslint-disable-line no-undef, new-cap
  if (!human) human = new Human.default(msg.data.config);
  const image = new ImageData(new Uint8ClampedArray(msg.data.image), msg.data.width, msg.data.height);
  let result = {};
  result = await human.detect(image, msg.data.config);
--- a/demo/nodejs/README.md
+++ b/demo/nodejs/README.md
@ -28,7 +28,8 @@ or you can pass a path to image to analyze, either on local filesystem or using
 node demo/nodejs/node.js
 ```
-```json
+<!-- eslint-skip -->
 ```js
 2021-06-01 08:52:15 INFO:  @vladmandic/human version 2.0.0
 2021-06-01 08:52:15 INFO:  User: vlado Platform: linux Arch: x64 Node: v16.0.0
 2021-06-01 08:52:15 INFO:  Current folder: /home/vlado/dev/human
@ -82,7 +83,7 @@ node demo/nodejs/node.js
    detector: { modelPath: 'handdetect.json' },
    skeleton: { modelPath: 'handskeleton.json' }
  },
-  object: { enabled: true, modelPath: 'mb3-centernet.json', minConfidence: 0.2, iouThreshold: 0.4, maxDetected: 10, skipFrames: 19 }
+  object: { enabled: true, modelPath: 'centernet.json', minConfidence: 0.2, iouThreshold: 0.4, maxDetected: 10, skipFrames: 19 }
 }
 08:52:15.673 Human: version: 2.0.0
 08:52:15.674 Human: tfjs version: 3.6.0
@ -96,7 +97,7 @@ node demo/nodejs/node.js
 08:52:15.847 Human: load model: file://models/handdetect.json
 08:52:15.847 Human: load model: file://models/handskeleton.json
 08:52:15.914 Human: load model: file://models/movenet-lightning.json
-08:52:15.957 Human: load model: file://models/mb3-centernet.json
+08:52:15.957 Human: load model: file://models/centernet.json
 08:52:16.015 Human: load model: file://models/faceres.json
 08:52:16.015 Human: tf engine state: 50796152 bytes 1318 tensors
 2021-06-01 08:52:16 INFO:  Loaded: [ 'face', 'movenet', 'handpose', 'emotion', 'centernet', 'faceres', [length]: 6 ]
--- a/demo/nodejs/node-bench.js
+++ b/demo/nodejs/node-bench.js
@ -0,0 +1,66 @@
 /**
 * Human simple demo for NodeJS
 */
 const childProcess = require('child_process'); // eslint-disable-line camelcase
 const log = require('@vladmandic/pilogger'); // eslint-disable-line node/no-unpublished-require
 const canvas = require('canvas'); // eslint-disable-line node/no-unpublished-require
 const config = {
  cacheSensitivity: 0.01,
  wasmPlatformFetch: true,
  modelBasePath: 'https://vladmandic.github.io/human-models/models/',
 };
 const count = 10;
 async function loadImage(input) {
  const inputImage = await canvas.loadImage(input);
  const inputCanvas = new canvas.Canvas(inputImage.width, inputImage.height);
  const inputCtx = inputCanvas.getContext('2d');
  inputCtx.drawImage(inputImage, 0, 0);
  const imageData = inputCtx.getImageData(0, 0, inputCanvas.width, inputCanvas.height);
  process.send({ input, resolution: [inputImage.width, inputImage.height] });
  return imageData;
 }
 async function runHuman(module, backend) {
  if (backend === 'wasm') require('@tensorflow/tfjs-backend-wasm'); // eslint-disable-line node/no-unpublished-require, global-require
  const Human = require('../../dist/' + module); // eslint-disable-line global-require, import/no-dynamic-require
  config.backend = backend;
  const human = new Human.Human(config);
  human.env.Canvas = canvas.Canvas;
  human.env.Image = canvas.Image;
  human.env.ImageData = canvas.ImageData;
  process.send({ human: human.version, module });
  await human.init();
  process.send({ desired: human.config.backend, wasm: human.env.wasm, tfjs: human.tf.version.tfjs, tensorflow: human.env.tensorflow });
  const imageData = await loadImage('samples/in/ai-body.jpg');
  const t0 = human.now();
  await human.load();
  const t1 = human.now();
  await human.warmup();
  const t2 = human.now();
  for (let i = 0; i < count; i++) await human.detect(imageData);
  const t3 = human.now();
  process.send({ backend: human.tf.getBackend(), load: Math.round(t1 - t0), warmup: Math.round(t2 - t1), detect: Math.round(t3 - t2), count, memory: human.tf.memory().numBytes });
 }
 async function executeWorker(args) {
  return new Promise((resolve) => {
    const worker = childProcess.fork(process.argv[1], args);
    worker.on('message', (msg) => log.data(msg));
    worker.on('exit', () => resolve(true));
  });
 }
 async function main() {
  if (process.argv[2]) {
    await runHuman(process.argv[2], process.argv[3]);
  } else {
    await executeWorker(['human.node.js', 'tensorflow']);
    await executeWorker(['human.node-gpu.js', 'tensorflow']);
    await executeWorker(['human.node-wasm.js', 'wasm']);
  }
 }
 main();
--- a/demo/nodejs/node-canvas.js
+++ b/demo/nodejs/node-canvas.js
@ -1,21 +1,22 @@
 /**
 * Human demo for NodeJS using Canvas library
 *
 * Requires [canvas](https://www.npmjs.com/package/canvas) to provide Canvas functionality in NodeJS environment
 */
 const fs = require('fs');
 const process = require('process');
-const log = require('@vladmandic/pilogger');
+const log = require('@vladmandic/pilogger'); // eslint-disable-line node/no-unpublished-require
-const canvas = require('canvas');
+// in nodejs environments tfjs-node is required to be loaded before human
-
+const tf = require('@tensorflow/tfjs-node'); // eslint-disable-line node/no-unpublished-require
-// eslint-disable-next-line import/no-extraneous-dependencies, no-unused-vars, @typescript-eslint/no-unused-vars
+const canvas = require('canvas'); // eslint-disable-line node/no-unpublished-require
-const tf = require('@tensorflow/tfjs-node'); // in nodejs environments tfjs-node is required to be loaded before human
+// const human = require('@vladmandic/human'); // use this when human is installed as module (majority of use cases)
 // const faceapi = require('@vladmandic/face-api'); // use this when human is installed as module (majority of use cases)
 const Human = require('../../dist/human.node.js'); // use this when using human in dev mode
 const config = { // just enable all and leave default settings
  debug: false,
-  face: { enabled: true }, // includes mesh, iris, emotion, descriptor
+  face: { enabled: true, detector: { maxDetected: 10 } }, // includes mesh, iris, emotion, descriptor
-  hand: { enabled: true, maxDetected: 2, minConfidence: 0.5, detector: { modelPath: 'handtrack.json' } }, // use alternative hand model
+  hand: { enabled: true, maxDetected: 20, minConfidence: 0.5, detector: { modelPath: 'handtrack.json' } }, // use alternative hand model
  body: { enabled: true },
  object: { enabled: true },
  gestures: { enabled: true },
@ -31,15 +32,16 @@ async function main() {
  // init
  const human = new Human.Human(config); // create instance of human
-  log.info('Human:', human.version);
+  log.info('Human:', human.version, 'TF:', tf.version_core);
  await human.load(); // pre-load models
-  log.info('Loaded models:', Object.keys(human.models).filter((a) => human.models[a]));
+  log.info('Loaded models:', human.models.loaded());
  log.info('Memory state:', human.tf.engine().memory());
  // parse cmdline
  const input = process.argv[2];
-  const output = process.argv[3];
+  let output = process.argv[3];
  if (!output.toLowerCase().endsWith('.jpg')) output += '.jpg';
  if (process.argv.length !== 4) log.error('Parameters: <input-image> <output-image> missing');
  else if (!fs.existsSync(input) && !input.startsWith('http')) log.error(`File not found: ${process.argv[2]}`);
  else {
@ -53,9 +55,6 @@ async function main() {
    // run detection
    const result = await human.detect(imageData);
    // run segmentation
    // const seg = await human.segmentation(inputCanvas);
    // log.data('Segmentation:', { data: seg.data.length, alpha: typeof seg.alpha, canvas: typeof seg.canvas });
    // print results summary
    const persons = result.persons; // invoke persons getter, only used to print summary on console
@ -63,7 +62,7 @@ async function main() {
      const face = persons[i].face;
      const faceTxt = face ? `score:${face.score} age:${face.age} gender:${face.gender} iris:${face.iris}` : null;
      const body = persons[i].body;
-      const bodyTxt = body ? `score:${body.score} keypoints:${body.keypoints?.length}` : null;
+      const bodyTxt = body ? `score:${body.score} keypoints:${body.keypoints.length}` : null;
      log.data(`Detected: #${i}: Face:${faceTxt} Body:${bodyTxt} LeftHand:${persons[i].hands.left ? 'yes' : 'no'} RightHand:${persons[i].hands.right ? 'yes' : 'no'} Gestures:${persons[i].gestures.length}`);
    }
@ -71,7 +70,6 @@ async function main() {
    const outputCanvas = new canvas.Canvas(inputImage.width, inputImage.height); // create canvas
    const outputCtx = outputCanvas.getContext('2d');
    outputCtx.drawImage(result.canvas || inputImage, 0, 0); // draw input image onto canvas
    // @ts-ignore canvas is not checked for typedefs
    human.draw.all(outputCanvas, result); // use human build-in method to draw results as overlays on canvas
    const outFile = fs.createWriteStream(output); // write canvas to new image file
    outFile.on('finish', () => log.state('Output image:', output, outputCanvas.width, outputCanvas.height));
--- a/demo/nodejs/node-event.js
+++ b/demo/nodejs/node-event.js
@ -2,15 +2,13 @@
 * Human demo for NodeJS
 */
 const log = require('@vladmandic/pilogger');
 const fs = require('fs');
 const process = require('process');
-let fetch; // fetch is dynamically imported later
+const log = require('@vladmandic/pilogger'); // eslint-disable-line node/no-unpublished-require
-
+// in nodejs environments tfjs-node is required to be loaded before human
-// eslint-disable-next-line import/no-extraneous-dependencies, no-unused-vars, @typescript-eslint/no-unused-vars
+const tf = require('@tensorflow/tfjs-node'); // eslint-disable-line node/no-unpublished-require
-const tf = require('@tensorflow/tfjs-node'); // in nodejs environments tfjs-node is required to be loaded before human
+// const human = require('@vladmandic/human'); // use this when human is installed as module (majority of use cases)
 // const faceapi = require('@vladmandic/face-api'); // use this when human is installed as module (majority of use cases)
 const Human = require('../../dist/human.node.js'); // use this when using human in dev mode
 let human = null;
@ -38,13 +36,13 @@ async function detect(input) {
  let buffer;
  log.info('Loading image:', input);
  if (input.startsWith('http:') || input.startsWith('https:')) {
    fetch = (await import('node-fetch')).default;
    const res = await fetch(input);
-    if (res && res.ok) buffer = await res.buffer();
+    if (res && res.ok) buffer = Buffer.from(await res.arrayBuffer());
    else log.error('Invalid image URL:', input, res.status, res.statusText, res.headers.get('content-type'));
  } else {
    buffer = fs.readFileSync(input);
  }
  log.data('Image bytes:', buffer?.length, 'buffer:', buffer?.slice(0, 32));
  // decode image using tfjs-node so we don't need external depenencies
  if (!buffer) return;
@ -59,6 +57,7 @@ async function main() {
  log.header();
  human = new Human.Human(myConfig);
  log.info('Human:', human.version, 'TF:', tf.version_core);
  if (human.events) {
    human.events.addEventListener('warmup', () => {
@ -66,8 +65,7 @@ async function main() {
    });
    human.events.addEventListener('load', () => {
-      const loaded = Object.keys(human.models).filter((a) => human.models[a]);
+      log.info('Event Loaded:', human.models.loaded(), human.tf.engine().memory());
      log.info('Event Loaded:', loaded, human.tf.engine().memory());
    });
    human.events.addEventListener('image', () => {
@ -79,7 +77,7 @@ async function main() {
      const persons = human.result.persons;
      for (let i = 0; i < persons.length; i++) {
        const face = persons[i].face;
-        const faceTxt = face ? `score:${face.score} age:${face.age} gender:${face.gender} iris:${face.iris}` : null;
+        const faceTxt = face ? `score:${face.score} age:${face.age} gender:${face.gender} iris:${face.distance}` : null;
        const body = persons[i].body;
        const bodyTxt = body ? `score:${body.score} keypoints:${body.keypoints?.length}` : null;
        log.data(`  #${i}: Face:${faceTxt} Body:${bodyTxt} LeftHand:${persons[i].hands.left ? 'yes' : 'no'} RightHand:${persons[i].hands.right ? 'yes' : 'no'} Gestures:${persons[i].gestures.length}`);
--- a/demo/nodejs/node-fetch.js
+++ b/demo/nodejs/node-fetch.js
@ -1,8 +1,14 @@
 /**
 * Human demo for NodeJS using http fetch to get image file
 *
 * Requires [node-fetch](https://www.npmjs.com/package/node-fetch) to provide `fetch` functionality in NodeJS environment
 */
 const fs = require('fs');
 const log = require('@vladmandic/pilogger'); // eslint-disable-line node/no-unpublished-require
-// eslint-disable-next-line import/no-extraneous-dependencies, no-unused-vars, @typescript-eslint/no-unused-vars
+// in nodejs environments tfjs-node is required to be loaded before human
-const tf = require('@tensorflow/tfjs-node'); // in nodejs environments tfjs-node is required to be loaded before human
+const tf = require('@tensorflow/tfjs-node'); // eslint-disable-line node/no-unpublished-require
-// const faceapi = require('@vladmandic/face-api'); // use this when human is installed as module (majority of use cases)
+// const human = require('@vladmandic/human'); // use this when human is installed as module (majority of use cases)
 const Human = require('../../dist/human.node.js'); // use this when using human in dev mode
 const humanConfig = {
@ -10,16 +16,15 @@ const humanConfig = {
 };
 async function main(inputFile) {
-  // @ts-ignore
+  global.fetch = (await import('node-fetch')).default; // eslint-disable-line node/no-unpublished-import, import/no-unresolved, node/no-missing-import, node/no-extraneous-import
  global.fetch = (await import('node-fetch')).default;
  const human = new Human.Human(humanConfig); // create instance of human using default configuration
  log.info('Human:', human.version, 'TF:', tf.version_core);
  await human.load(); // optional as models would be loaded on-demand first time they are required
  await human.warmup(); // optional as model warmup is performed on-demand first time its executed
  const buffer = fs.readFileSync(inputFile); // read file data into buffer
  const tensor = human.tf.node.decodeImage(buffer); // decode jpg data
  const result = await human.detect(tensor); // run detection; will initialize backend and on-demand load models
-  // eslint-disable-next-line no-console
+  log.data(result.gesture);
  console.log(result.gesture);
 }
 main('samples/in/ai-body.jpg');
--- a/demo/nodejs/node-similarity.js
+++ b/demo/nodejs/node-similarity.js
@ -2,13 +2,13 @@
 * Human Person Similarity test for NodeJS
 */
 const log = require('@vladmandic/pilogger');
 const fs = require('fs');
 const process = require('process');
-// eslint-disable-next-line import/no-extraneous-dependencies, no-unused-vars, @typescript-eslint/no-unused-vars
+const log = require('@vladmandic/pilogger'); // eslint-disable-line node/no-unpublished-require
-const tf = require('@tensorflow/tfjs-node'); // in nodejs environments tfjs-node is required to be loaded before human
+// in nodejs environments tfjs-node is required to be loaded before human
-// const faceapi = require('@vladmandic/face-api'); // use this when human is installed as module (majority of use cases)
+const tf = require('@tensorflow/tfjs-node'); // eslint-disable-line node/no-unpublished-require
 // const human = require('@vladmandic/human'); // use this when human is installed as module (majority of use cases)
 const Human = require('../../dist/human.node.js'); // use this when using human in dev mode
 let human = null;
@ -25,21 +25,19 @@ const myConfig = {
 async function init() {
  human = new Human.Human(myConfig);
  await human.tf.ready();
-  log.info('Human:', human.version);
+  log.info('Human:', human.version, 'TF:', tf.version_core);
  await human.load();
-  const loaded = Object.keys(human.models).filter((a) => human.models[a]);
+  log.info('Loaded:', human.models.loaded());
  log.info('Loaded:', loaded);
  log.info('Memory state:', human.tf.engine().memory());
 }
 async function detect(input) {
  if (!fs.existsSync(input)) {
-    log.error('Cannot load image:', input);
+    throw new Error('Cannot load image:', input);
    process.exit(1);
  }
  const buffer = fs.readFileSync(input);
  const tensor = human.tf.node.decodeImage(buffer, 3);
-  log.state('Loaded image:', input, tensor['shape']);
+  log.state('Loaded image:', input, tensor.shape);
  const result = await human.detect(tensor, myConfig);
  human.tf.dispose(tensor);
  log.state('Detected faces:', result.face.length);
@ -51,16 +49,15 @@ async function main() {
  log.header();
  if (process.argv.length !== 4) {
    log.error('Parameters: <first image> <second image> missing');
-    process.exit(1);
+    return;
  }
  await init();
  const res1 = await detect(process.argv[2]);
  const res2 = await detect(process.argv[3]);
  if (!res1 || !res1.face || res1.face.length === 0 || !res2 || !res2.face || res2.face.length === 0) {
-    log.error('Could not detect face descriptors');
+    throw new Error('Could not detect face descriptors');
    process.exit(1);
  }
-  const similarity = human.similarity(res1.face[0].embedding, res2.face[0].embedding, { order: 2 });
+  const similarity = human.match.similarity(res1.face[0].embedding, res2.face[0].embedding, { order: 2 });
  log.data('Similarity: ', similarity);
 }
--- a/demo/nodejs/node-simple.js
+++ b/demo/nodejs/node-simple.js
@ -1,19 +1,32 @@
-const fs = require('fs');
+/**
 * Human simple demo for NodeJS
 */
-// eslint-disable-next-line import/no-extraneous-dependencies, no-unused-vars, @typescript-eslint/no-unused-vars
+const fs = require('fs');
-const tf = require('@tensorflow/tfjs-node'); // in nodejs environments tfjs-node is required to be loaded before human
+const process = require('process');
-// const faceapi = require('@vladmandic/face-api'); // use this when human is installed as module (majority of use cases)
+
 // in nodejs environments tfjs-node is required to be loaded before human
 const tf = require('@tensorflow/tfjs-node'); // eslint-disable-line node/no-unpublished-require
 // const human = require('@vladmandic/human'); // use this when human is installed as module (majority of use cases)
 const Human = require('../../dist/human.node.js'); // use this when using human in dev mode
-async function main(inputFile) {
+const humanConfig = {
-  const human = new Human.Human(); // create instance of human using default configuration
+  // add any custom config here
  debug: true,
  body: { enabled: false },
 };
 async function detect(inputFile) {
  const human = new Human.Human(humanConfig); // create instance of human using default configuration
  console.log('Human:', human.version, 'TF:', tf.version_core); // eslint-disable-line no-console
  await human.load(); // optional as models would be loaded on-demand first time they are required
  await human.warmup(); // optional as model warmup is performed on-demand first time its executed
  const buffer = fs.readFileSync(inputFile); // read file data into buffer
  const tensor = human.tf.node.decodeImage(buffer); // decode jpg data
  console.log('loaded input file:', inputFile, 'resolution:', tensor.shape); // eslint-disable-line no-console
  const result = await human.detect(tensor); // run detection; will initialize backend and on-demand load models
-  // eslint-disable-next-line no-console
+  console.log(result); // eslint-disable-line no-console
  console.log(result);
 }
-main('samples/in/ai-body.jpg');
+if (process.argv.length === 3) detect(process.argv[2]); // if input file is provided as cmdline parameter use it
 else detect('samples/in/ai-body.jpg'); // else use built-in test inputfile
--- a/demo/nodejs/node-video.js
+++ b/demo/nodejs/node-video.js
@ -7,24 +7,24 @@
 * If you want process at specific intervals, set output fps to some value
 * If you want to process an input stream, set real-time flag and set input as required
 *
- * Note that pipe2jpeg is not part of Human dependencies and should be installed manually
+ * Note that [pipe2jpeg](https://www.npmjs.com/package/pipe2jpeg) is not part of Human dependencies and should be installed manually
- * Working version of ffmpeg must be present on the system
+ * Working version of `ffmpeg` must be present on the system
 */
 const process = require('process');
 const spawn = require('child_process').spawn;
-const log = require('@vladmandic/pilogger');
+const log = require('@vladmandic/pilogger'); // eslint-disable-line node/no-unpublished-require
-// @ts-ignore pipe2jpeg is not installed by default
+// in nodejs environments tfjs-node is required to be loaded before human
-// eslint-disable-next-line node/no-missing-require
+// const tf = require('@tensorflow/tfjs-node'); // eslint-disable-line node/no-unpublished-require
-const Pipe2Jpeg = require('pipe2jpeg');
+// const human = require('@vladmandic/human'); // use this when human is installed as module (majority of use cases)
-
+const Pipe2Jpeg = require('pipe2jpeg'); // eslint-disable-line node/no-missing-require, import/no-unresolved
-// eslint-disable-next-line import/no-extraneous-dependencies, no-unused-vars, @typescript-eslint/no-unused-vars
+// const human = require('@vladmandic/human'); // use this when human is installed as module (majority of use cases)
 const tf = require('@tensorflow/tfjs-node'); // in nodejs environments tfjs-node is required to be loaded before human
 // const faceapi = require('@vladmandic/face-api'); // use this when human is installed as module (majority of use cases)
 const Human = require('../../dist/human.node.js'); // use this when using human in dev mode
 let count = 0; // counter
 let busy = false; // busy flag
-const inputFile = './test.mp4';
+let inputFile = './test.mp4';
 if (process.argv.length === 3) inputFile = process.argv[2];
 const humanConfig = {
  modelBasePath: 'file://models/',
@ -61,15 +61,16 @@ const ffmpegParams = [
  'pipe:1', // output to unix pipe that is then captured by pipe2jpeg
 ];
-async function process(jpegBuffer) {
+async function detect(jpegBuffer) {
  if (busy) return; // skip processing if busy
  busy = true;
  const tensor = human.tf.node.decodeJpeg(jpegBuffer, 3); // decode jpeg buffer to raw tensor
  log.state('input frame:', ++count, 'size:', jpegBuffer.length, 'decoded shape:', tensor.shape);
  const res = await human.detect(tensor);
-  log.data('gesture', JSON.stringify(res.gesture));
+  human.tf.dispose(tensor); // must dispose tensor
-  // do processing here
+  // start custom processing here
-  tf.dispose(tensor); // must dispose tensor
+  log.data('frame', { frame: ++count, size: jpegBuffer.length, shape: tensor.shape, face: res?.face?.length, body: res?.body?.length, hand: res?.hand?.length, gesture: res?.gesture?.length });
  if (res?.face?.[0]) log.data('person', { score: [res.face[0].boxScore, res.face[0].faceScore], age: res.face[0].age || 0, gender: [res.face[0].genderScore || 0, res.face[0].gender], emotion: res.face[0].emotion?.[0] });
  // at the of processing mark loop as not busy so it can process next frame
  busy = false;
 }
@ -77,8 +78,9 @@ async function main() {
  log.header();
  await human.tf.ready();
  // pre-load models
-  log.info('human:', human.version);
+  log.info({ human: human.version, tf: human.tf.version_core });
-  pipe2jpeg.on('jpeg', (jpegBuffer) => process(jpegBuffer));
+  log.info({ input: inputFile });
  pipe2jpeg.on('data', (jpegBuffer) => detect(jpegBuffer));
  const ffmpeg = spawn('ffmpeg', ffmpegParams, { stdio: ['ignore', 'pipe', 'ignore'] });
  ffmpeg.on('error', (error) => log.error('ffmpeg error:', error));
--- a/demo/nodejs/node-webcam.js
+++ b/demo/nodejs/node-webcam.js
@ -2,19 +2,17 @@
 * Human demo for NodeJS
 * Unsupported sample of using external utility fswebcam to capture screenshot from attached webcam in regular intervals and process it using Human
 *
- * Note that node-webcam is not part of Human dependencies and should be installed manually
+ * Note that [node-webcam](https://www.npmjs.com/package/node-webcam) is not part of Human dependencies and should be installed manually
- * Working version of fswebcam must be present on the system
+ * Working version of `fswebcam` must be present on the system
 */
 let initial = true; // remember if this is the first run to print additional details
-const log = require('@vladmandic/pilogger');
+const log = require('@vladmandic/pilogger'); // eslint-disable-line node/no-unpublished-require
-// @ts-ignore node-webcam is not installed by default
+const nodeWebCam = require('node-webcam'); // eslint-disable-line import/no-unresolved, node/no-missing-require
 // eslint-disable-next-line node/no-missing-require
 const nodeWebCam = require('node-webcam');
-// eslint-disable-next-line import/no-extraneous-dependencies, no-unused-vars, @typescript-eslint/no-unused-vars
+// in nodejs environments tfjs-node is required to be loaded before human
-const tf = require('@tensorflow/tfjs-node'); // in nodejs environments tfjs-node is required to be loaded before human
+const tf = require('@tensorflow/tfjs-node'); // eslint-disable-line node/no-unpublished-require
-// const faceapi = require('@vladmandic/face-api'); // use this when human is installed as module (majority of use cases)
+// const human = require('@vladmandic/human'); // use this when human is installed as module (majority of use cases)
 const Human = require('../../dist/human.node.js'); // use this when using human in dev mode
 // options for node-webcam
@ -29,6 +27,7 @@ const camera = nodeWebCam.create(optionsCamera);
 const optionsHuman = {
  modelBasePath: 'file://models/',
 };
 const human = new Human.Human(optionsHuman);
 function buffer2tensor(buffer) {
@ -61,8 +60,8 @@ async function detect() {
    } else {
      const tensor = buffer2tensor(data); // create tensor from image buffer
      if (initial) log.data('input tensor:', tensor.shape);
-      // eslint-disable-next-line promise/no-promise-in-callback
+      human.detect(tensor) // eslint-disable-line promise/no-promise-in-callback
-      human.detect(tensor).then((result) => {
+        .then((result) => {
          if (result && result.face && result.face.length > 0) {
            for (let i = 0; i < result.face.length; i++) {
              const face = result.face[i];
@ -72,7 +71,9 @@ async function detect() {
          } else {
            log.data('  Face: N/A');
          }
-      });
+          return result;
        })
        .catch(() => log.error('human detect error'));
    }
    initial = false;
  });
@ -81,6 +82,7 @@ async function detect() {
 }
 async function main() {
  log.info('human:', human.version, 'tf:', tf.version_core);
  camera.list((list) => {
    log.data('detected camera:', list);
  });
--- a/demo/nodejs/node.js
+++ b/demo/nodejs/node.js
@ -2,16 +2,14 @@
 * Human demo for NodeJS
 */
 const log = require('@vladmandic/pilogger');
 const fs = require('fs');
 const path = require('path');
 const process = require('process');
 const log = require('@vladmandic/pilogger'); // eslint-disable-line node/no-unpublished-require
-let fetch; // fetch is dynamically imported later
+// in nodejs environments tfjs-node is required to be loaded before human
-
+const tf = require('@tensorflow/tfjs-node'); // eslint-disable-line node/no-unpublished-require
-// eslint-disable-next-line import/no-extraneous-dependencies, no-unused-vars, @typescript-eslint/no-unused-vars
+// const human = require('@vladmandic/human'); // use this when human is installed as module (majority of use cases)
 const tf = require('@tensorflow/tfjs-node'); // in nodejs environments tfjs-node is required to be loaded before human
 // const faceapi = require('@vladmandic/face-api'); // use this when human is installed as module (majority of use cases)
 const Human = require('../../dist/human.node.js'); // use this when using human in dev mode
 let human = null;
@ -46,14 +44,14 @@ async function init() {
  human = new Human.Human(myConfig);
  // wait until tf is ready
  await human.tf.ready();
  log.info('human:', human.version, 'tf:', tf.version_core);
  // pre-load models
  log.info('Human:', human.version);
  // log.info('Active Configuration', human.config);
  await human.load();
-  const loaded = Object.keys(human.models).filter((a) => human.models[a]);
+  log.info('Loaded:', human.models.loaded());
  log.info('Loaded:', loaded);
  // log.info('Memory state:', human.tf.engine().memory());
-  log.data(tf.backend()['binding'] ? tf.backend()['binding']['TF_Version'] : null);
+  log.data(tf.backend().binding ? tf.backend().binding.TF_Version : null);
 }
 async function detect(input) {
@ -62,11 +60,12 @@ async function detect(input) {
  log.info('Loading image:', input);
  if (input.startsWith('http:') || input.startsWith('https:')) {
    const res = await fetch(input);
-    if (res && res.ok) buffer = await res.buffer();
+    if (res && res.ok) buffer = Buffer.from(await res.arrayBuffer());
    else log.error('Invalid image URL:', input, res.status, res.statusText, res.headers.get('content-type'));
  } else {
    buffer = fs.readFileSync(input);
  }
  log.data('Image bytes:', buffer?.length, 'buffer:', buffer?.slice(0, 32));
  // decode image using tfjs-node so we don't need external depenencies
  // can also be done using canvas.js or some other 3rd party image library
@ -86,14 +85,14 @@ async function detect(input) {
  });
  // image shape contains image dimensions and depth
-  log.state('Processing:', tensor['shape']);
+  log.state('Processing:', tensor.shape);
  // run actual detection
  let result;
  try {
    result = await human.detect(tensor, myConfig);
  } catch (err) {
-    log.error('caught');
+    log.error('caught', err);
  }
  // dispose image tensor as we no longer need it
@ -105,7 +104,7 @@ async function detect(input) {
    for (let i = 0; i < result.face.length; i++) {
      const face = result.face[i];
      const emotion = face.emotion.reduce((prev, curr) => (prev.score > curr.score ? prev : curr));
-      log.data(`  Face: #${i} boxScore:${face.boxScore} faceScore:${face.faceScore} age:${face.age} genderScore:${face.genderScore} gender:${face.gender} emotionScore:${emotion.score} emotion:${emotion.emotion} iris:${face.iris}`);
+      log.data(`  Face: #${i} boxScore:${face.boxScore} faceScore:${face.faceScore} age:${face.age} genderScore:${face.genderScore} gender:${face.gender} emotionScore:${emotion.score} emotion:${emotion.emotion} distance:${face.distance}`);
    }
  } else {
    log.data('  Face: N/A');
@ -189,7 +188,6 @@ async function main() {
  log.configure({ inspect: { breakLength: 265 } });
  log.header();
  log.info('Current folder:', process.env.PWD);
  fetch = (await import('node-fetch')).default;
  await init();
  const f = process.argv[2];
  if (process.argv.length !== 3) {
@ -197,8 +195,7 @@ async function main() {
    await test();
  } else if (!fs.existsSync(f) && !f.startsWith('http')) {
    log.error(`File not found: ${process.argv[2]}`);
-  } else {
+  } else if (fs.existsSync(f)) {
    if (fs.existsSync(f)) {
    const stat = fs.statSync(f);
    if (stat.isDirectory()) {
      const dir = fs.readdirSync(f);
@ -212,6 +209,5 @@ async function main() {
    await detect(f);
  }
 }
 }
 main();
--- a/demo/nodejs/process-folder.js
+++ b/demo/nodejs/process-folder.js
@ -1,56 +1,64 @@
 /**
 * Human demo for NodeJS
 *
 * Takes input and output folder names parameters and processes all images
 * found in input folder and creates annotated images in output folder
 *
 * Requires [canvas](https://www.npmjs.com/package/canvas) to provide Canvas functionality in NodeJS environment
 */
 const fs = require('fs');
 const path = require('path');
 const process = require('process');
-const log = require('@vladmandic/pilogger');
+const log = require('@vladmandic/pilogger'); // eslint-disable-line node/no-unpublished-require
-const canvas = require('canvas');
+const canvas = require('canvas'); // eslint-disable-line node/no-unpublished-require
-// const tf = require('@tensorflow/tfjs-node-gpu'); // for nodejs, `tfjs-node` or `tfjs-node-gpu` should be loaded before using Human
+// for nodejs, `tfjs-node` or `tfjs-node-gpu` should be loaded before using Human
 const tf = require('@tensorflow/tfjs-node-gpu'); // eslint-disable-line node/no-unpublished-require
 const Human = require('../../dist/human.node-gpu.js'); // this is 'const Human = require('../dist/human.node-gpu.js').default;'
 const config = { // just enable all and leave default settings
  modelBasePath: 'file://models',
  debug: true,
-  async: false,
+  softwareKernels: true, // slower but enhanced precision since face rotation can work in software mode in nodejs environments
-  cacheSensitivity: 0,
+  cacheSensitivity: 0.01,
-  face: { enabled: true, detector: { maxDetected: 20 } },
+  face: { enabled: true, detector: { maxDetected: 100, minConfidence: 0.1 } },
-  object: { enabled: true },
+  object: { enabled: true, maxDetected: 100, minConfidence: 0.1 },
  gesture: { enabled: true },
-  hand: { enabled: true },
+  hand: { enabled: true, maxDetected: 100, minConfidence: 0.2 },
-  body: { enabled: true, modelPath: 'https://vladmandic.github.io/human-models/models/movenet-multipose.json' },
+  body: { enabled: true, maxDetected: 100, minConfidence: 0.1, modelPath: 'https://vladmandic.github.io/human-models/models/movenet-multipose.json' },
 };
-async function main() {
+const poolSize = 4;
  log.header();
  globalThis.Canvas = canvas.Canvas; // patch global namespace with canvas library
  globalThis.ImageData = canvas.ImageData; // patch global namespace with canvas library
 const human = new Human.Human(config); // create instance of human
  log.info('Human:', human.version);
  const configErrors = await human.validate();
  if (configErrors.length > 0) log.error('Configuration errors:', configErrors);
  await human.load(); // pre-load models
  log.info('Loaded models:', Object.keys(human.models).filter((a) => human.models[a]));
-  const inDir = process.argv[2];
+async function saveFile(shape, buffer, result, outFile) {
-  const outDir = process.argv[3];
+  return new Promise(async (resolve, reject) => { // eslint-disable-line no-async-promise-executor
-  if (process.argv.length !== 4) {
+    const outputCanvas = new canvas.Canvas(shape[2], shape[1]); // create canvas
-    log.error('Parameters: <input-directory> <output-directory> missing');
+    const outputCtx = outputCanvas.getContext('2d');
-    return;
+    const inputImage = await canvas.loadImage(buffer); // load image using canvas library
-  }
+    outputCtx.drawImage(inputImage, 0, 0); // draw input image onto canvas
-  if (!fs.existsSync(inDir) || !fs.statSync(inDir).isDirectory() || !fs.existsSync(outDir) || !fs.statSync(outDir).isDirectory()) {
+    human.draw.all(outputCanvas, result); // use human build-in method to draw results as overlays on canvas
-    log.error('Invalid directory specified:', 'input:', fs.existsSync(inDir) ?? fs.statSync(inDir).isDirectory(), 'output:', fs.existsSync(outDir) ?? fs.statSync(outDir).isDirectory());
+    const outStream = fs.createWriteStream(outFile); // write canvas to new image file
-    return;
+    outStream.on('finish', () => {
      log.data('Output image:', outFile, outputCanvas.width, outputCanvas.height);
      resolve();
    });
    outStream.on('error', (err) => {
      log.error('Output error:', outFile, err);
      reject();
    });
    const stream = outputCanvas.createJPEGStream({ quality: 0.5, progressive: true, chromaSubsampling: true });
    stream.pipe(outStream);
  });
 }
-  const dir = fs.readdirSync(inDir);
+async function processFile(image, inFile, outFile) {
  const images = dir.filter((f) => fs.statSync(path.join(inDir, f)).isFile() && (f.toLocaleLowerCase().endsWith('.jpg') || f.toLocaleLowerCase().endsWith('.jpeg')));
  log.info(`Processing folder: ${inDir} entries:`, dir.length, 'images', images.length);
  for (const image of images) {
    const inFile = path.join(inDir, image);
  const buffer = fs.readFileSync(inFile);
-    const tensor = human.tf.tidy(() => {
+  const tensor = tf.tidy(() => {
-      const decode = human.tf.node.decodeImage(buffer, 3);
+    const decode = tf.node.decodeImage(buffer, 3);
-      const expand = human.tf.expandDims(decode, 0);
+    const expand = tf.expandDims(decode, 0);
-      const cast = human.tf.cast(expand, 'float32');
+    const cast = tf.cast(expand, 'float32');
    return cast;
  });
  log.state('Loaded image:', inFile, tensor.shape);
@ -59,20 +67,53 @@ async function main() {
  human.tf.dispose(tensor);
  log.data(`Detected: ${image}:`, 'Face:', result.face.length, 'Body:', result.body.length, 'Hand:', result.hand.length, 'Objects:', result.object.length, 'Gestures:', result.gesture.length);
-    const outputCanvas = new canvas.Canvas(tensor.shape[2], tensor.shape[1]); // create canvas
+  if (outFile) await saveFile(tensor.shape, buffer, result, outFile);
    const outputCtx = outputCanvas.getContext('2d');
    const inputImage = await canvas.loadImage(buffer); // load image using canvas library
    outputCtx.drawImage(inputImage, 0, 0); // draw input image onto canvas
    // @ts-ignore
    human.draw.all(outputCanvas, result); // use human build-in method to draw results as overlays on canvas
    const outFile = path.join(outDir, image);
    const outStream = fs.createWriteStream(outFile); // write canvas to new image file
    outStream.on('finish', () => log.state('Output image:', outFile, outputCanvas.width, outputCanvas.height));
    outStream.on('error', (err) => log.error('Output error:', outFile, err));
    const stream = outputCanvas.createJPEGStream({ quality: 0.5, progressive: true, chromaSubsampling: true });
    // @ts-ignore
    stream.pipe(outStream);
 }
 async function main() {
  log.header();
  globalThis.Canvas = canvas.Canvas; // patch global namespace with canvas library
  globalThis.ImageData = canvas.ImageData; // patch global namespace with canvas library
  log.info('Human:', human.version, 'TF:', tf.version_core);
  const configErrors = await human.validate();
  if (configErrors.length > 0) log.error('Configuration errors:', configErrors);
  await human.load(); // pre-load models
  log.info('Loaded models:', human.models.loaded());
  const inDir = process.argv[2];
  const outDir = process.argv[3];
  if (!inDir) {
    log.error('Parameters: <input-directory> missing');
    return;
  }
  if (inDir && (!fs.existsSync(inDir) || !fs.statSync(inDir).isDirectory())) {
    log.error('Invalid input directory:', fs.existsSync(inDir) ?? fs.statSync(inDir).isDirectory());
    return;
  }
  if (!outDir) {
    log.info('Parameters: <output-directory> missing, images will not be saved');
  }
  if (outDir && (!fs.existsSync(outDir) || !fs.statSync(outDir).isDirectory())) {
    log.error('Invalid output directory:', fs.existsSync(outDir) ?? fs.statSync(outDir).isDirectory());
    return;
  }
  const dir = fs.readdirSync(inDir);
  const images = dir.filter((f) => fs.statSync(path.join(inDir, f)).isFile() && (f.toLocaleLowerCase().endsWith('.jpg') || f.toLocaleLowerCase().endsWith('.jpeg')));
  log.info(`Processing folder: ${inDir} entries:`, dir.length, 'images', images.length);
  const t0 = performance.now();
  const promises = [];
  for (let i = 0; i < images.length; i++) {
    const inFile = path.join(inDir, images[i]);
    const outFile = outDir ? path.join(outDir, images[i]) : null;
    promises.push(processFile(images[i], inFile, outFile));
    if (i % poolSize === 0) await Promise.all(promises);
  }
  await Promise.all(promises);
  const t1 = performance.now();
  log.info(`Processed ${images.length} images in ${Math.round(t1 - t0)} ms`);
 }
 main();
--- a/demo/segmentation/index.html
+++ b/demo/segmentation/index.html
@ -0,0 +1,61 @@
 <!DOCTYPE html>
 <html lang="en">
  <head>
    <meta charset="utf-8">
    <meta http-equiv="content-type" content="text/html; charset=utf-8">
    <title>Human Demo</title>
    <meta name="viewport" content="width=device-width, shrink-to-fit=yes">
    <meta name="mobile-web-app-capable" content="yes">
    <meta name="application-name" content="Human Demo">
    <meta name="keywords" content="Human Demo">
    <meta name="description" content="Human Demo; Author: Vladimir Mandic <mandic00@live.com>">
    <link rel="manifest" href="../manifest.webmanifest">
    <link rel="shortcut icon" href="../favicon.ico" type="image/x-icon">
    <link rel="icon" sizes="256x256" href="../assets/icons/dash-256.png">
    <link rel="apple-touch-icon" href="../assets/icons/dash-256.png">
    <link rel="apple-touch-startup-image" href="../assets/icons/dash-256.png">
    <style>
      @font-face { font-family: 'CenturyGothic'; font-display: swap; font-style: normal; font-weight: 400; src: local('CenturyGothic'), url('../assets/century-gothic.ttf') format('truetype'); }
      html { font-size: 18px; }
      body { font-size: 1rem; font-family: "CenturyGothic", "Segoe UI", sans-serif; font-variant: small-caps; width: -webkit-fill-available; height: 100%; background: black; color: white; overflow: hidden; margin: 0; }
      select { font-size: 1rem; font-family: "CenturyGothic", "Segoe UI", sans-serif; font-variant: small-caps; background: gray; color: white; border: none; }
    </style>
    <script src="../segmentation/index.js" type="module"></script>
  </head>
  <body>
    <noscript><h1>javascript is required</h1></noscript>
    <nav>
      <div id="nav" class="nav"></div>
    </nav>
    <header>
      <div id="header" class="header" style="position: fixed; top: 0; right: 0; padding: 4px; margin: 16px; background: rgba(0, 0, 0, 0.5); z-index: 10; line-height: 2rem;">
        <label for="mode">mode</label>
        <select id="mode" name="mode">
          <option value="default">remove background</option>
          <option value="alpha">draw alpha channel</option>
          <option value="foreground">full foreground</option>
          <option value="state">recurrent state</option>
        </select><br>
        <label for="composite">composite</label>
        <select id="composite" name="composite"></select><br>
        <label for="ratio">downsample ratio</label>
        <input type="range" name="ratio" id="ratio" min="0.1" max="1" value="0.5" step="0.05">
        <div id="fps" style="margin-top: 8px"></div>
      </div>
    </header>
    <main>
      <div id="main" class="main">
        <video id="webcam" style="position: fixed; top: 0; left: 0; width: 50vw; height: 50vh"></video>
        <img id="background" alt="background" style="position: fixed; top: 0; right: 0; width: 50vw; height: 50vh" controls></img>
        <canvas id="output" style="position: fixed; bottom: 0; left: 0; height: 50vh"></canvas>
        <canvas id="merge" style="position: fixed; bottom: 0; right: 0; height: 50vh"></canvas>
      </div>
    </main>
    <footer>
      <div id="footer" class="footer"></div>
    </footer>
    <aside>
      <div id="aside" class="aside"></div>
    </aside>
  </body>
 </html>
--- a/demo/segmentation/index.js
+++ b/demo/segmentation/index.js
@ -0,0 +1,99 @@
 /**
 * Human demo for browsers
 * @default Human Library
 * @summary <https://github.com/vladmandic/human>
 * @author <https://github.com/vladmandic>
 * @copyright <https://github.com/vladmandic>
 * @license MIT
 */
 import * as H from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human
 const humanConfig = { // user configuration for human, used to fine-tune behavior
  modelBasePath: 'https://vladmandic.github.io/human-models/models/',
  filter: { enabled: true, equalization: false, flip: false },
  face: { enabled: false },
  body: { enabled: false },
  hand: { enabled: false },
  object: { enabled: false },
  gesture: { enabled: false },
  segmentation: {
    enabled: true,
    modelPath: 'rvm.json', // can use rvm, selfie or meet
    ratio: 0.5,
    mode: 'default',
  },
 };
 const backgroundImage = '../../samples/in/background.jpg';
 const human = new H.Human(humanConfig); // create instance of human with overrides from user configuration
 const log = (...msg) => console.log(...msg); // eslint-disable-line no-console
 async function main() {
  // gather dom elements
  const dom = {
    background: document.getElementById('background'),
    webcam: document.getElementById('webcam'),
    output: document.getElementById('output'),
    merge: document.getElementById('merge'),
    mode: document.getElementById('mode'),
    composite: document.getElementById('composite'),
    ratio: document.getElementById('ratio'),
    fps: document.getElementById('fps'),
  };
  // set defaults
  dom.fps.innerText = 'initializing';
  dom.ratio.valueAsNumber = human.config.segmentation.ratio;
  dom.background.src = backgroundImage;
  dom.composite.innerHTML = ['source-atop', 'color', 'color-burn', 'color-dodge', 'copy', 'darken', 'destination-atop', 'destination-in', 'destination-out', 'destination-over', 'difference', 'exclusion', 'hard-light', 'hue', 'lighten', 'lighter', 'luminosity', 'multiply', 'overlay', 'saturation', 'screen', 'soft-light', 'source-in', 'source-out', 'source-over', 'xor'].map((gco) => `<option value="${gco}">${gco}</option>`).join(''); // eslint-disable-line max-len
  const ctxMerge = dom.merge.getContext('2d');
  log('human version:', human.version, '| tfjs version:', human.tf.version['tfjs-core']);
  log('platform:', human.env.platform, '| agent:', human.env.agent);
  await human.load(); // preload all models
  log('backend:', human.tf.getBackend(), '| available:', human.env.backends);
  log('models stats:', human.models.stats());
  log('models loaded:', human.models.loaded());
  await human.warmup(); // warmup function to initialize backend for future faster detection
  const numTensors = human.tf.engine().state.numTensors;
  // initialize webcam
  dom.webcam.onplay = () => { // start processing on video play
    log('start processing');
    dom.output.width = human.webcam.width;
    dom.output.height = human.webcam.height;
    dom.merge.width = human.webcam.width;
    dom.merge.height = human.webcam.height;
    loop(); // eslint-disable-line no-use-before-define
  };
  await human.webcam.start({ element: dom.webcam, crop: true, width: window.innerWidth / 2, height: window.innerHeight / 2 }); // use human webcam helper methods and associate webcam stream with a dom element
  if (!human.webcam.track) dom.fps.innerText = 'webcam error';
  // processing loop
  async function loop() {
    if (!human.webcam.element || human.webcam.paused) return; // check if webcam is valid and playing
    human.config.segmentation.mode = dom.mode.value; // get segmentation mode from ui
    human.config.segmentation.ratio = dom.ratio.valueAsNumber; // get segmentation downsample ratio from ui
    const t0 = Date.now();
    const rgba = await human.segmentation(human.webcam.element, human.config); // run model and process results
    const t1 = Date.now();
    if (!rgba) {
      dom.fps.innerText = 'error';
      return;
    }
    dom.fps.innerText = `fps: ${Math.round(10000 / (t1 - t0)) / 10}`; // mark performance
    human.draw.tensor(rgba, dom.output); // draw raw output
    human.tf.dispose(rgba); // dispose tensors
    ctxMerge.globalCompositeOperation = 'source-over';
    ctxMerge.drawImage(dom.background, 0, 0); // draw original video to first stacked canvas
    ctxMerge.globalCompositeOperation = dom.composite.value;
    ctxMerge.drawImage(dom.output, 0, 0); // draw processed output to second stacked canvas
    if (numTensors !== human.tf.engine().state.numTensors) log({ leak: human.tf.engine().state.numTensors - numTensors }); // check for memory leaks
    requestAnimationFrame(loop);
  }
 }
 window.onload = main;
--- a/demo/tracker/README.md
+++ b/demo/tracker/README.md
@ -0,0 +1,28 @@
 ## Tracker
 ### Based on
 <https://github.com/opendatacam/node-moving-things-tracker>  
 ### Build
 - remove reference to `lodash`:  
  > `isEqual` in <tracker.js>  
 - replace external lib:  
  > curl https://raw.githubusercontent.com/ubilabs/kd-tree-javascript/master/kdTree.js -o lib/kdTree-min.js
 - build with `esbuild`:  
  > node_modules/.bin/esbuild --bundle tracker.js --format=esm --platform=browser --target=esnext --keep-names --tree-shaking=false --analyze --outfile=/home/vlado/dev/human/demo/tracker/tracker.js --banner:js="/* eslint-disable */"
 ### Usage
    computeDistance(item1, item2)
    disableKeepInMemory()
    enableKeepInMemory()
    getAllTrackedItems()
    getJSONDebugOfTrackedItems(roundInt = true)
    getJSONOfAllTrackedItems()
    getJSONOfTrackedItems(roundInt = true)
    getTrackedItemsInMOTFormat(frameNb)
    reset()
    setParams(newParams)
    updateTrackedItemsWithNewFrame(detectionsOfThisFrame, frameNb)
--- a/demo/tracker/index.html
+++ b/demo/tracker/index.html
@ -0,0 +1,65 @@
 <!DOCTYPE html>
 <html lang="en">
  <head>
    <meta charset="utf-8">
    <title>Human</title>
    <meta name="viewport" content="width=device-width" id="viewport">
    <meta name="keywords" content="Human">
    <meta name="application-name" content="Human">
    <meta name="description" content="Human: 3D Face Detection, Body Pose, Hand & Finger Tracking, Iris Tracking, Age & Gender Prediction, Emotion Prediction & Gesture Recognition; Author: Vladimir Mandic <https://github.com/vladmandic>">
    <meta name="msapplication-tooltip" content="Human: 3D Face Detection, Body Pose, Hand & Finger Tracking, Iris Tracking, Age & Gender Prediction, Emotion Prediction & Gesture Recognition; Author: Vladimir Mandic <https://github.com/vladmandic>">
    <meta name="theme-color" content="#000000">
    <link rel="manifest" href="../manifest.webmanifest">
    <link rel="shortcut icon" href="../../favicon.ico" type="image/x-icon">
    <link rel="apple-touch-icon" href="../../assets/icon.png">
    <script src="./index.js" type="module"></script>
    <style>
      html { font-family: 'Segoe UI'; font-size: 16px; font-variant: small-caps; }
      body { margin: 0; background: black; color: white; overflow-x: hidden; width: 100vw; height: 100vh; }
      body::-webkit-scrollbar { display: none; }
      input[type="file"] { font-family: 'Segoe UI'; font-size: 14px; font-variant: small-caps; }
      ::-webkit-file-upload-button { background: #333333; color: white; border: 0; border-radius: 0; padding: 6px 16px; box-shadow: 4px 4px 4px #222222; font-family: 'Segoe UI'; font-size: 14px; font-variant: small-caps; }
    </style>
  </head>
  <body>
    <div style="display: flex">
      <video id="video" playsinline style="width: 25vw" controls controlslist="nofullscreen nodownload noremoteplayback" disablepictureinpicture loop></video>
      <canvas id="canvas" style="width: 75vw"></canvas> 
    </div>
    <div class="uploader" style="padding: 8px">
      <input type="file" name="inputvideo" id="inputvideo" accept="video/*"></input>
      <input type="checkbox" id="interpolation" name="interpolation"></input>
      <label for="tracker">interpolation</label>
    </div>
    <form id="config" style="padding: 8px; line-height: 1.6rem;">
      tracker | 
      <input type="checkbox" id="tracker" name="tracker" checked></input>
      <label for="tracker">enabled</label> | 
      <input type="checkbox" id="keepInMemory" name="keepInMemory"></input>
      <label for="keepInMemory">keepInMemory</label> | 
      <br>
      tracker source | 
      <input type="radio" id="box-face" name="box" value="face" checked>
      <label for="box-face">face</label> |
      <input type="radio" id="box-body" name="box" value="body">
      <label for="box-face">body</label> |
      <input type="radio" id="box-object" name="box" value="object">
      <label for="box-face">object</label> |
      <br>
      tracker config | 
      <input type="range" id="unMatchedFramesTolerance" name="unMatchedFramesTolerance" min="0" max="300" step="1", value="60"></input>
      <label for="unMatchedFramesTolerance">unMatchedFramesTolerance</label> |
      <input type="range" id="iouLimit" name="unMatchedFramesTolerance" min="0" max="1" step="0.01", value="0.1"></input>
      <label for="iouLimit">iouLimit</label> |
      <input type="range" id="distanceLimit" name="unMatchedFramesTolerance" min="0" max="1" step="0.01", value="0.1"></input>
      <label for="distanceLimit">distanceLimit</label> |
      <input type="radio" id="matchingAlgorithm-kdTree" name="matchingAlgorithm" value="kdTree" checked>
      <label for="matchingAlgorithm-kdTree">kdTree</label> |
      <input type="radio" id="matchingAlgorithm-munkres" name="matchingAlgorithm" value="munkres">
      <label for="matchingAlgorithm-kdTree">munkres</label> |
    </form>
    <pre id="status" style="position: absolute; top: 12px; right: 20px; background-color: grey; padding: 8px; box-shadow: 2px 2px black"></pre>
    <pre id="log" style="padding: 8px"></pre>
    <div id="performance" style="position: absolute; bottom: 0; width: 100%; padding: 8px; font-size: 0.8rem;"></div>
  </body>
 </html>
--- a/demo/tracker/index.js
+++ b/demo/tracker/index.js
--- a/demo/tracker/index.js.map
+++ b/demo/tracker/index.js.map
--- a/demo/tracker/index.ts
+++ b/demo/tracker/index.ts
@ -0,0 +1,208 @@
 /**
 * Human demo for browsers
 * @default Human Library
 * @summary <https://github.com/vladmandic/human>
 * @author <https://github.com/vladmandic>
 * @copyright <https://github.com/vladmandic>
 * @license MIT
 */
 import * as H from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human
 import tracker from './tracker.js';
 const humanConfig: Partial<H.Config> = { // user configuration for human, used to fine-tune behavior
  debug: true,
  backend: 'webgl',
  // cacheSensitivity: 0,
  // cacheModels: false,
  // warmup: 'none',
  modelBasePath: 'https://vladmandic.github.io/human-models/models',
  filter: { enabled: true, equalization: false, flip: false },
  face: {
    enabled: true,
    detector: { rotation: false, maxDetected: 10, minConfidence: 0.3 },
    mesh: { enabled: true },
    attention: { enabled: false },
    iris: { enabled: false },
    description: { enabled: false },
    emotion: { enabled: false },
    antispoof: { enabled: false },
    liveness: { enabled: false },
  },
  body: { enabled: false, maxDetected: 6, modelPath: 'movenet-multipose.json' },
  hand: { enabled: false },
  object: { enabled: false, maxDetected: 10 },
  segmentation: { enabled: false },
  gesture: { enabled: false },
 };
 interface TrackerConfig {
  unMatchedFramesTolerance: number, // number of frame when an object is not matched before considering it gone; ignored if fastDelete is set
  iouLimit: number, // exclude things from beeing matched if their IOU less than; 1 means total overlap; 0 means no overlap
  fastDelete: boolean, // remove new objects immediately if they could not be matched in the next frames; if set, ignores unMatchedFramesTolerance
  distanceLimit: number, // distance limit for matching; if values need to be excluded from matching set their distance to something greater than the distance limit
  matchingAlgorithm: 'kdTree' | 'munkres', // algorithm used to match tracks with new detections
 }
 interface TrackerResult {
  id: number,
  confidence: number,
  bearing: number,
  isZombie: boolean,
  name: string,
  x: number,
  y: number,
  w: number,
  h: number,
 }
 const trackerConfig: TrackerConfig = {
  unMatchedFramesTolerance: 100,
  iouLimit: 0.05,
  fastDelete: false,
  distanceLimit: 1e4,
  matchingAlgorithm: 'kdTree',
 };
 const human = new H.Human(humanConfig); // create instance of human with overrides from user configuration
 const dom = { // grab instances of dom objects so we dont have to look them up later
  video: document.getElementById('video') as HTMLVideoElement,
  canvas: document.getElementById('canvas') as HTMLCanvasElement,
  log: document.getElementById('log') as HTMLPreElement,
  fps: document.getElementById('status') as HTMLPreElement,
  tracker: document.getElementById('tracker') as HTMLInputElement,
  interpolation: document.getElementById('interpolation') as HTMLInputElement,
  config: document.getElementById('config') as HTMLFormElement,
  ctx: (document.getElementById('canvas') as HTMLCanvasElement).getContext('2d') as CanvasRenderingContext2D,
 };
 const timestamp = { detect: 0, draw: 0, tensors: 0, start: 0 }; // holds information used to calculate performance and possible memory leaks
 const fps = { detectFPS: 0, drawFPS: 0, frames: 0, averageMs: 0 }; // holds calculated fps information for both detect and screen refresh
 const log = (...msg) => { // helper method to output messages
  dom.log.innerText += msg.join(' ') + '\n';
  console.log(...msg); // eslint-disable-line no-console
 };
 const status = (msg) => dom.fps.innerText = msg; // print status element
 async function detectionLoop() { // main detection loop
  if (!dom.video.paused && dom.video.readyState >= 2) {
    if (timestamp.start === 0) timestamp.start = human.now();
    // log('profiling data:', await human.profile(dom.video));
    await human.detect(dom.video, humanConfig); // actual detection; were not capturing output in a local variable as it can also be reached via human.result
    const tensors = human.tf.memory().numTensors; // check current tensor usage for memory leaks
    if (tensors - timestamp.tensors !== 0) log('allocated tensors:', tensors - timestamp.tensors); // printed on start and each time there is a tensor leak
    timestamp.tensors = tensors;
    fps.detectFPS = Math.round(1000 * 1000 / (human.now() - timestamp.detect)) / 1000;
    fps.frames++;
    fps.averageMs = Math.round(1000 * (human.now() - timestamp.start) / fps.frames) / 1000;
  }
  timestamp.detect = human.now();
  requestAnimationFrame(detectionLoop); // start new frame immediately
 }
 function drawLoop() { // main screen refresh loop
  if (!dom.video.paused && dom.video.readyState >= 2) {
    const res: H.Result = dom.interpolation.checked ? human.next(human.result) : human.result; // interpolate results if enabled
    let tracking: H.FaceResult[] | H.BodyResult[] | H.ObjectResult[] = [];
    if (human.config.face.enabled) tracking = res.face;
    else if (human.config.body.enabled) tracking = res.body;
    else if (human.config.object.enabled) tracking = res.object;
    else log('unknown object type');
    let data: TrackerResult[] = [];
    if (dom.tracker.checked) {
      const items = tracking.map((obj) => ({
        x: obj.box[0] + obj.box[2] / 2,
        y: obj.box[1] + obj.box[3] / 2,
        w: obj.box[2],
        h: obj.box[3],
        name: obj.label || (human.config.face.enabled ? 'face' : 'body'),
        confidence: obj.score,
      }));
      tracker.updateTrackedItemsWithNewFrame(items, fps.frames);
      data = tracker.getJSONOfTrackedItems(true) as TrackerResult[];
    }
    human.draw.canvas(dom.video, dom.canvas); // copy input video frame to output canvas
    for (let i = 0; i < tracking.length; i++) {
      // @ts-ignore
      const name = tracking[i].label || (human.config.face.enabled ? 'face' : 'body');
      dom.ctx.strokeRect(tracking[i].box[0], tracking[i].box[1], tracking[i].box[1], tracking[i].box[2]);
      dom.ctx.fillText(`id: ${tracking[i].id} ${Math.round(100 * tracking[i].score)}% ${name}`, tracking[i].box[0] + 4, tracking[i].box[1] + 16);
      if (data[i]) {
        dom.ctx.fillText(`t: ${data[i].id} ${Math.round(100 * data[i].confidence)}% ${data[i].name} ${data[i].isZombie ? 'zombie' : ''}`, tracking[i].box[0] + 4, tracking[i].box[1] + 34);
      }
    }
  }
  const now = human.now();
  fps.drawFPS = Math.round(1000 * 1000 / (now - timestamp.draw)) / 1000;
  timestamp.draw = now;
  status(dom.video.paused ? 'paused' : `fps: ${fps.detectFPS.toFixed(1).padStart(5, ' ')} detect | ${fps.drawFPS.toFixed(1).padStart(5, ' ')} draw`); // write status
  setTimeout(drawLoop, 30); // use to slow down refresh from max refresh rate to target of 30 fps
 }
 async function handleVideo(file: File) {
  const url = URL.createObjectURL(file);
  dom.video.src = url;
  await dom.video.play();
  log('loaded video:', file.name, 'resolution:', [dom.video.videoWidth, dom.video.videoHeight], 'duration:', dom.video.duration);
  dom.canvas.width = dom.video.videoWidth;
  dom.canvas.height = dom.video.videoHeight;
  dom.ctx.strokeStyle = 'white';
  dom.ctx.fillStyle = 'white';
  dom.ctx.font = '16px Segoe UI';
  dom.video.playbackRate = 0.25;
 }
 function initInput() {
  document.body.addEventListener('dragenter', (evt) => evt.preventDefault());
  document.body.addEventListener('dragleave', (evt) => evt.preventDefault());
  document.body.addEventListener('dragover', (evt) => evt.preventDefault());
  document.body.addEventListener('drop', async (evt) => {
    evt.preventDefault();
    if (evt.dataTransfer) evt.dataTransfer.dropEffect = 'copy';
    const file = evt.dataTransfer?.files?.[0];
    if (file) await handleVideo(file);
    log(dom.video.readyState);
  });
  (document.getElementById('inputvideo') as HTMLInputElement).onchange = async (evt) => {
    evt.preventDefault();
    const file = evt.target?.['files']?.[0];
    if (file) await handleVideo(file);
  };
  dom.config.onchange = () => {
    trackerConfig.distanceLimit = (document.getElementById('distanceLimit') as HTMLInputElement).valueAsNumber;
    trackerConfig.iouLimit = (document.getElementById('iouLimit') as HTMLInputElement).valueAsNumber;
    trackerConfig.unMatchedFramesTolerance = (document.getElementById('unMatchedFramesTolerance') as HTMLInputElement).valueAsNumber;
    trackerConfig.unMatchedFramesTolerance = (document.getElementById('unMatchedFramesTolerance') as HTMLInputElement).valueAsNumber;
    trackerConfig.matchingAlgorithm = (document.getElementById('matchingAlgorithm-kdTree') as HTMLInputElement).checked ? 'kdTree' : 'munkres';
    tracker.setParams(trackerConfig);
    if ((document.getElementById('keepInMemory') as HTMLInputElement).checked) tracker.enableKeepInMemory();
    else tracker.disableKeepInMemory();
    tracker.reset();
    log('tracker config change', JSON.stringify(trackerConfig));
    humanConfig.face!.enabled = (document.getElementById('box-face') as HTMLInputElement).checked; // eslint-disable-line @typescript-eslint/no-non-null-assertion
    humanConfig.body!.enabled = (document.getElementById('box-body') as HTMLInputElement).checked; // eslint-disable-line @typescript-eslint/no-non-null-assertion
    humanConfig.object!.enabled = (document.getElementById('box-object') as HTMLInputElement).checked; // eslint-disable-line @typescript-eslint/no-non-null-assertion
  };
  dom.tracker.onchange = (evt) => {
    log('tracker', (evt.target as HTMLInputElement).checked ? 'enabled' : 'disabled');
    tracker.setParams(trackerConfig);
    tracker.reset();
  };
 }
 async function main() { // main entry point
  log('human version:', human.version, '| tfjs version:', human.tf.version['tfjs-core']);
  log('platform:', human.env.platform, '| agent:', human.env.agent);
  status('loading...');
  await human.load(); // preload all models
  log('backend:', human.tf.getBackend(), '| available:', human.env.backends);
  log('models loaded:', human.models.loaded());
  status('initializing...');
  await human.warmup(); // warmup function to initialize backend for future faster detection
  initInput(); // initialize input
  await detectionLoop(); // start detection loop
  drawLoop(); // start draw loop
 }
 window.onload = main;
--- a/demo/tracker/tracker.js
+++ b/demo/tracker/tracker.js
--- a/demo/typescript/index.html
+++ b/demo/typescript/index.html
@ -21,7 +21,7 @@
    </style>
  </head>
  <body>
-    <canvas id="canvas" style="margin: 0 auto; width: 100%"></canvas>
+    <canvas id="canvas" style="margin: 0 auto; width: 100vw"></canvas>
    <video id="video" playsinline style="display: none"></video>
    <pre id="status" style="position: absolute; top: 12px; right: 20px; background-color: grey; padding: 8px; box-shadow: 2px 2px black"></pre>
    <pre id="log" style="padding: 8px"></pre>
--- a/demo/typescript/index.js
+++ b/demo/typescript/index.js
@ -4,106 +4,6 @@
  author: <https://github.com/vladmandic>'
 */
-// demo/typescript/index.ts
+import*as m from"../../dist/human.esm.js";var v=1920,b={debug:!0,backend:"webgl",modelBasePath:"https://vladmandic.github.io/human-models/models/",filter:{enabled:!0,equalization:!1,flip:!1},face:{enabled:!0,detector:{rotation:!1},mesh:{enabled:!0},attention:{enabled:!1},iris:{enabled:!0},description:{enabled:!0},emotion:{enabled:!0},antispoof:{enabled:!0},liveness:{enabled:!0}},body:{enabled:!1},hand:{enabled:!1},object:{enabled:!1},segmentation:{enabled:!1},gesture:{enabled:!0}},e=new m.Human(b);e.env.perfadd=!1;e.draw.options.font='small-caps 18px "Lato"';e.draw.options.lineHeight=20;e.draw.options.drawPoints=!0;var a={video:document.getElementById("video"),canvas:document.getElementById("canvas"),log:document.getElementById("log"),fps:document.getElementById("status"),perf:document.getElementById("performance")},n={detect:0,draw:0,tensors:0,start:0},s={detectFPS:0,drawFPS:0,frames:0,averageMs:0},o=(...t)=>{a.log.innerText+=t.join(" ")+`
-import { Human } from "../../dist/human.esm.js";
+`,console.log(...t)},i=t=>a.fps.innerText=t,g=t=>a.perf.innerText="tensors:"+e.tf.memory().numTensors.toString()+" | performance: "+JSON.stringify(t).replace(/"|{|}/g,"").replace(/,/g," | ");async function f(){if(!a.video.paused){n.start===0&&(n.start=e.now()),await e.detect(a.video);let t=e.tf.memory().numTensors;t-n.tensors!==0&&o("allocated tensors:",t-n.tensors),n.tensors=t,s.detectFPS=Math.round(1e3*1e3/(e.now()-n.detect))/1e3,s.frames++,s.averageMs=Math.round(1e3*(e.now()-n.start)/s.frames)/1e3,s.frames%100===0&&!a.video.paused&&o("performance",{...s,tensors:n.tensors})}n.detect=e.now(),requestAnimationFrame(f)}async function u(){var d,r,c;if(!a.video.paused){let l=e.next(e.result),w=await e.image(a.video);e.draw.canvas(w.canvas,a.canvas);let p={bodyLabels:`person confidence [score] and ${(c=(r=(d=e.result)==null?void 0:d.body)==null?void 0:r[0])==null?void 0:c.keypoints.length} keypoints`};await e.draw.all(a.canvas,l,p),g(l.performance)}let t=e.now();s.drawFPS=Math.round(1e3*1e3/(t-n.draw))/1e3,n.draw=t,i(a.video.paused?"paused":`fps: ${s.detectFPS.toFixed(1).padStart(5," ")} detect | ${s.drawFPS.toFixed(1).padStart(5," ")} draw`),setTimeout(u,30)}async function h(){let d=(await e.webcam.enumerate())[0].deviceId,r=await e.webcam.start({element:a.video,crop:!1,width:v,id:d});o(r),a.canvas.width=e.webcam.width,a.canvas.height=e.webcam.height,a.canvas.onclick=async()=>{e.webcam.paused?await e.webcam.play():e.webcam.pause()}}async function y(){o("human version:",e.version,"| tfjs version:",e.tf.version["tfjs-core"]),o("platform:",e.env.platform,"| agent:",e.env.agent),i("loading..."),await e.load(),o("backend:",e.tf.getBackend(),"| available:",e.env.backends),o("models stats:",e.models.stats()),o("models loaded:",e.models.loaded()),o("environment",e.env),i("initializing..."),await e.warmup(),await h(),await f(),await u()}window.onload=y;
 var humanConfig = {
  modelBasePath: "../../models",
  filter: { enabled: true, equalization: false },
  face: { enabled: true, detector: { rotation: false }, mesh: { enabled: true }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true } },
  body: { enabled: true },
  hand: { enabled: true },
  object: { enabled: false },
  gesture: { enabled: true }
 };
 var human = new Human(humanConfig);
 human.env["perfadd"] = false;
 human.draw.options.font = 'small-caps 18px "Lato"';
 human.draw.options.lineHeight = 20;
 var dom = {
  video: document.getElementById("video"),
  canvas: document.getElementById("canvas"),
  log: document.getElementById("log"),
  fps: document.getElementById("status"),
  perf: document.getElementById("performance")
 };
 var timestamp = { detect: 0, draw: 0, tensors: 0 };
 var fps = { detect: 0, draw: 0 };
 var log = (...msg) => {
  dom.log.innerText += msg.join(" ") + "\n";
  console.log(...msg);
 };
 var status = (msg) => dom.fps.innerText = msg;
 var perf = (msg) => dom.perf.innerText = "tensors:" + human.tf.memory().numTensors + " | performance: " + JSON.stringify(msg).replace(/"|{|}/g, "").replace(/,/g, " | ");
 async function webCam() {
  status("starting webcam...");
  const options = { audio: false, video: { facingMode: "user", resizeMode: "none", width: { ideal: document.body.clientWidth } } };
  const stream = await navigator.mediaDevices.getUserMedia(options);
  const ready = new Promise((resolve) => {
    dom.video.onloadeddata = () => resolve(true);
  });
  dom.video.srcObject = stream;
  dom.video.play();
  await ready;
  dom.canvas.width = dom.video.videoWidth;
  dom.canvas.height = dom.video.videoHeight;
  const track = stream.getVideoTracks()[0];
  const capabilities = track.getCapabilities ? track.getCapabilities() : "";
  const settings = track.getSettings ? track.getSettings() : "";
  const constraints = track.getConstraints ? track.getConstraints() : "";
  log("video:", dom.video.videoWidth, dom.video.videoHeight, track.label, { stream, track, settings, constraints, capabilities });
  dom.canvas.onclick = () => {
    if (dom.video.paused)
      dom.video.play();
    else
      dom.video.pause();
  };
 }
 async function detectionLoop() {
  if (!dom.video.paused) {
    await human.detect(dom.video);
    const tensors = human.tf.memory().numTensors;
    if (tensors - timestamp.tensors !== 0)
      log("allocated tensors:", tensors - timestamp.tensors);
    timestamp.tensors = tensors;
  }
  const now = human.now();
  fps.detect = 1e3 / (now - timestamp.detect);
  timestamp.detect = now;
  requestAnimationFrame(detectionLoop);
 }
 async function drawLoop() {
  if (!dom.video.paused) {
    const interpolated = await human.next(human.result);
    await human.draw.canvas(dom.video, dom.canvas);
    await human.draw.all(dom.canvas, interpolated);
    perf(interpolated.performance);
  }
  const now = human.now();
  fps.draw = 1e3 / (now - timestamp.draw);
  timestamp.draw = now;
  status(dom.video.paused ? "paused" : `fps: ${fps.detect.toFixed(1).padStart(5, " ")} detect | ${fps.draw.toFixed(1).padStart(5, " ")} draw`);
  setTimeout(drawLoop, 30);
 }
 async function main() {
  log("human version:", human.version, "| tfjs version:", human.tf.version["tfjs-core"]);
  log("platform:", human.env.platform, "| agent:", human.env.agent);
  status("loading...");
  await human.load();
  log("backend:", human.tf.getBackend(), "| available:", human.env.backends);
  log("loaded models:", Object.values(human.models).filter((model) => model !== null).length);
  status("initializing...");
  await human.warmup();
  await webCam();
  await detectionLoop();
  await drawLoop();
 }
 window.onload = main;
 /**
 * Human demo for browsers
 * @default Human Library
 * @summary <https://github.com/vladmandic/human>
 * @author <https://github.com/vladmandic>
 * @copyright <https://github.com/vladmandic>
 * @license MIT
 */
 //# sourceMappingURL=index.js.map
--- a/demo/typescript/index.js.map
+++ b/demo/typescript/index.js.map
--- a/demo/typescript/index.ts
+++ b/demo/typescript/index.ts
@ -7,25 +7,34 @@
 * @license MIT
 */
-import { Human, Config } from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human
+import * as H from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human
-const humanConfig: Partial<Config> = { // user configuration for human, used to fine-tune behavior
+const width = 1920; // used by webcam config as well as human maximum resultion // can be anything, but resolutions higher than 4k will disable internal optimizations
-  // backend: 'webgpu' as const,
+
-  // async: true,
+const humanConfig: Partial<H.Config> = { // user configuration for human, used to fine-tune behavior
-  modelBasePath: '../../models',
+  debug: true,
-  filter: { enabled: true, equalization: false },
+  backend: 'webgl',
-  face: { enabled: true, detector: { rotation: false }, mesh: { enabled: true }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true } },
+  // cacheSensitivity: 0,
-  body: { enabled: true },
+  // cacheModels: false,
-  hand: { enabled: true },
+  // warmup: 'none',
  // modelBasePath: '../../models',
  modelBasePath: 'https://vladmandic.github.io/human-models/models/',
  filter: { enabled: true, equalization: false, flip: false },
  face: { enabled: true, detector: { rotation: false }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true }, antispoof: { enabled: true }, liveness: { enabled: true } },
  body: { enabled: false },
  hand: { enabled: false },
  object: { enabled: false },
  segmentation: { enabled: false },
  gesture: { enabled: true },
 };
-const human = new Human(humanConfig); // create instance of human with overrides from user configuration
+const human = new H.Human(humanConfig); // create instance of human with overrides from user configuration
-human.env['perfadd'] = false; // is performance data showing instant or total values
+human.env.perfadd = false; // is performance data showing instant or total values
 human.draw.options.font = 'small-caps 18px "Lato"'; // set font used to draw labels when using draw methods
 human.draw.options.lineHeight = 20;
 human.draw.options.drawPoints = true; // draw points on face mesh
 // human.draw.options.fillPolygons = true;
 const dom = { // grab instances of dom objects so we dont have to look them up later
  video: document.getElementById('video') as HTMLVideoElement,
@ -34,75 +43,72 @@ const dom = { // grab instances of dom objects so we dont have to look them up l
  fps: document.getElementById('status') as HTMLPreElement,
  perf: document.getElementById('performance') as HTMLDivElement,
 };
-const timestamp = { detect: 0, draw: 0, tensors: 0 }; // holds information used to calculate performance and possible memory leaks
+const timestamp = { detect: 0, draw: 0, tensors: 0, start: 0 }; // holds information used to calculate performance and possible memory leaks
-const fps = { detect: 0, draw: 0 }; // holds calculated fps information for both detect and screen refresh
+const fps = { detectFPS: 0, drawFPS: 0, frames: 0, averageMs: 0 }; // holds calculated fps information for both detect and screen refresh
 const log = (...msg) => { // helper method to output messages
  dom.log.innerText += msg.join(' ') + '\n';
-  // eslint-disable-next-line no-console
+  console.log(...msg); // eslint-disable-line no-console
  console.log(...msg);
 };
 const status = (msg) => dom.fps.innerText = msg; // print status element
-const perf = (msg) => dom.perf.innerText = 'tensors:' + human.tf.memory().numTensors + ' | performance: ' + JSON.stringify(msg).replace(/"|{|}/g, '').replace(/,/g, ' | '); // print performance element
+const perf = (msg) => dom.perf.innerText = 'tensors:' + human.tf.memory().numTensors.toString() + ' | performance: ' + JSON.stringify(msg).replace(/"|{|}/g, '').replace(/,/g, ' | '); // print performance element
 async function webCam() { // initialize webcam
  status('starting webcam...');
  // @ts-ignore resizeMode is not yet defined in tslib
  const options: MediaStreamConstraints = { audio: false, video: { facingMode: 'user', resizeMode: 'none', width: { ideal: document.body.clientWidth } } };
  const stream: MediaStream = await navigator.mediaDevices.getUserMedia(options);
  const ready = new Promise((resolve) => { dom.video.onloadeddata = () => resolve(true); });
  dom.video.srcObject = stream;
  dom.video.play();
  await ready;
  dom.canvas.width = dom.video.videoWidth;
  dom.canvas.height = dom.video.videoHeight;
  const track: MediaStreamTrack = stream.getVideoTracks()[0];
  const capabilities: MediaTrackCapabilities | string = track.getCapabilities ? track.getCapabilities() : '';
  const settings: MediaTrackSettings | string = track.getSettings ? track.getSettings() : '';
  const constraints: MediaTrackConstraints | string = track.getConstraints ? track.getConstraints() : '';
  log('video:', dom.video.videoWidth, dom.video.videoHeight, track.label, { stream, track, settings, constraints, capabilities });
  dom.canvas.onclick = () => { // pause when clicked on screen and resume on next click
    if (dom.video.paused) dom.video.play();
    else dom.video.pause();
  };
 }
 async function detectionLoop() { // main detection loop
  if (!dom.video.paused) {
-    // console.log('profiling data:', await human.profile(dom.video));
+    if (timestamp.start === 0) timestamp.start = human.now();
    // log('profiling data:', await human.profile(dom.video));
    await human.detect(dom.video); // actual detection; were not capturing output in a local variable as it can also be reached via human.result
    const tensors = human.tf.memory().numTensors; // check current tensor usage for memory leaks
    if (tensors - timestamp.tensors !== 0) log('allocated tensors:', tensors - timestamp.tensors); // printed on start and each time there is a tensor leak
    timestamp.tensors = tensors;
    fps.detectFPS = Math.round(1000 * 1000 / (human.now() - timestamp.detect)) / 1000;
    fps.frames++;
    fps.averageMs = Math.round(1000 * (human.now() - timestamp.start) / fps.frames) / 1000;
    if (fps.frames % 100 === 0 && !dom.video.paused) log('performance', { ...fps, tensors: timestamp.tensors });
  }
-  const now = human.now();
+  timestamp.detect = human.now();
  fps.detect = 1000 / (now - timestamp.detect);
  timestamp.detect = now;
  requestAnimationFrame(detectionLoop); // start new frame immediately
 }
 async function drawLoop() { // main screen refresh loop
  if (!dom.video.paused) {
-    const interpolated = await human.next(human.result); // smoothen result using last-known results
+    const interpolated = human.next(human.result); // smoothen result using last-known results
-    await human.draw.canvas(dom.video, dom.canvas); // draw canvas to screen
+    const processed = await human.image(dom.video); // get current video frame, but enhanced with human.filters
-    await human.draw.all(dom.canvas, interpolated); // draw labels, boxes, lines, etc.
+    human.draw.canvas(processed.canvas as HTMLCanvasElement, dom.canvas);
    const opt: Partial<H.DrawOptions> = { bodyLabels: `person confidence [score] and ${human.result?.body?.[0]?.keypoints.length} keypoints` };
    await human.draw.all(dom.canvas, interpolated, opt); // draw labels, boxes, lines, etc.
    perf(interpolated.performance); // write performance data
  }
  const now = human.now();
-  fps.draw = 1000 / (now - timestamp.draw);
+  fps.drawFPS = Math.round(1000 * 1000 / (now - timestamp.draw)) / 1000;
  timestamp.draw = now;
-  status(dom.video.paused ? 'paused' : `fps: ${fps.detect.toFixed(1).padStart(5, ' ')} detect | ${fps.draw.toFixed(1).padStart(5, ' ')} draw`); // write status
+  status(dom.video.paused ? 'paused' : `fps: ${fps.detectFPS.toFixed(1).padStart(5, ' ')} detect | ${fps.drawFPS.toFixed(1).padStart(5, ' ')} draw`); // write status
  // requestAnimationFrame(drawLoop); // refresh at screen refresh rate
  setTimeout(drawLoop, 30); // use to slow down refresh from max refresh rate to target of 30 fps
 }
 async function webCam() {
  const devices = await human.webcam.enumerate();
  const id = devices[0].deviceId; // use first available video source
  const webcamStatus = await human.webcam.start({ element: dom.video, crop: false, width, id }); // use human webcam helper methods and associate webcam stream with a dom element
  log(webcamStatus);
  dom.canvas.width = human.webcam.width;
  dom.canvas.height = human.webcam.height;
  dom.canvas.onclick = async () => { // pause when clicked on screen and resume on next click
    if (human.webcam.paused) await human.webcam.play();
    else human.webcam.pause();
  };
 }
 async function main() { // main entry point
  log('human version:', human.version, '| tfjs version:', human.tf.version['tfjs-core']);
  log('platform:', human.env.platform, '| agent:', human.env.agent);
  status('loading...');
  await human.load(); // preload all models
  log('backend:', human.tf.getBackend(), '| available:', human.env.backends);
-  log('loaded models:', Object.values(human.models).filter((model) => model !== null).length);
+  log('models stats:', human.models.stats());
  log('models loaded:', human.models.loaded());
  log('environment', human.env);
  status('initializing...');
  await human.warmup(); // warmup function to initialize backend for future faster detection
  await webCam(); // start webcam
--- a/demo/video/index.html
+++ b/demo/video/index.html
@ -0,0 +1,58 @@
 <!DOCTYPE html>
 <html lang="en">
  <head>
    <meta charset="utf-8">
    <title>Human</title>
    <meta name="viewport" content="width=device-width" id="viewport">
    <meta name="keywords" content="Human">
    <meta name="description" content="Human: Demo; Author: Vladimir Mandic <https://github.com/vladmandic>">
    <link rel="manifest" href="../manifest.webmanifest">
    <link rel="shortcut icon" href="../../favicon.ico" type="image/x-icon">
    <style>
      @font-face { font-family: 'Lato'; font-display: swap; font-style: normal; font-weight: 100; src: local('Lato'), url('../../assets/lato-light.woff2') }
      body { font-family: 'Lato', 'Segoe UI'; font-size: 16px; font-variant: small-caps; margin: 0; background: black; color: white; overflow: hidden; width: 100vw; height: 100vh; }
    </style>
  </head>
  <body>
    <canvas id="canvas" style="margin: 0 auto; width: 100%"></canvas>
    <pre id="log" style="padding: 8px; position: fixed; bottom: 0"></pre>
    <script type="module">
      import * as H from '../../dist/human.esm.js'; // equivalent of import @vladmandic/Human
      const humanConfig = { // user configuration for human, used to fine-tune behavior
        modelBasePath: '../../models', // models can be loaded directly from cdn as well
        filter: { enabled: true, equalization: true, flip: false },
        face: { enabled: true, detector: { rotation: false }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true } },
        body: { enabled: true },
        hand: { enabled: true },
        gesture: { enabled: true },
        object: { enabled: false },
        segmentation: { enabled: false },
      };
      const human = new H.Human(humanConfig); // create instance of human with overrides from user configuration
      const canvas = document.getElementById('canvas'); // output canvas to draw both webcam and detection results
      async function drawLoop() { // main screen refresh loop
        const interpolated = human.next(); // get smoothened result using last-known results which are continously updated based on input webcam video
        human.draw.canvas(human.webcam.element, canvas); // draw webcam video to screen canvas // better than using procesed image as this loop happens faster than processing loop
        await human.draw.all(canvas, interpolated); // draw labels, boxes, lines, etc.
        setTimeout(drawLoop, 30); // use to slow down refresh from max refresh rate to target of 1000/30 ~ 30 fps
      }
      async function main() { // main entry point
        document.getElementById('log').innerHTML = `human version: ${human.version} | tfjs version: ${human.tf.version['tfjs-core']}<br>platform: ${human.env.platform} | agent ${human.env.agent}`;
        await human.webcam.start({ crop: true }); // find webcam and start it
        human.video(human.webcam.element); // instruct human to continously detect video frames
        canvas.width = human.webcam.width; // set canvas resolution to input webcam native resolution
        canvas.height = human.webcam.height;
        canvas.onclick = async () => { // pause when clicked on screen and resume on next click
          if (human.webcam.paused) await human.webcam.play();
          else human.webcam.pause();
        };
        await drawLoop(); // start draw loop
      }
      window.onload = main;
    </script>  
  </body>
 </html>
--- a/dist/human.d.ts
+++ b/dist/human.d.ts
--- a/dist/human.esm-nobundle.d.ts
+++ b/dist/human.esm-nobundle.d.ts
--- a/dist/human.esm-nobundle.js
+++ b/dist/human.esm-nobundle.js
--- a/dist/human.esm-nobundle.js.map
+++ b/dist/human.esm-nobundle.js.map
--- a/dist/human.esm.d.ts
+++ b/dist/human.esm.d.ts
--- a/dist/human.esm.js
+++ b/dist/human.esm.js
--- a/dist/human.esm.js.map
+++ b/dist/human.esm.js.map
--- a/dist/human.js
+++ b/dist/human.js
--- a/dist/human.node-gpu.d.ts
+++ b/dist/human.node-gpu.d.ts
--- a/dist/human.node-gpu.js
+++ b/dist/human.node-gpu.js
--- a/dist/human.node-wasm.d.ts
+++ b/dist/human.node-wasm.d.ts
--- a/dist/human.node-wasm.js
+++ b/dist/human.node-wasm.js
--- a/dist/human.node.d.ts
+++ b/dist/human.node.d.ts
--- a/dist/human.node.js
+++ b/dist/human.node.js
--- a/dist/tfjs.esm.d.ts
+++ b/dist/tfjs.esm.d.ts
@ -0,0 +1,25 @@
 /* eslint-disable import/no-unresolved */
 /* eslint-disable import/no-extraneous-dependencies */
 export * from 'types/tfjs.esm';
 export declare const version: {
  'tfjs-core': string;
  'tfjs-backend-cpu': string;
  'tfjs-backend-webgl': string;
  'tfjs-data': string;
  'tfjs-layers': string;
  'tfjs-converter': string;
  tfjs: string;
 };
 export * from '@tensorflow/tfjs-core';
 export * from '@tensorflow/tfjs-converter';
 export * from '@tensorflow/tfjs-data';
 export * from '@tensorflow/tfjs-layers';
 export * from '@tensorflow/tfjs-backend-cpu';
 export * from '@tensorflow/tfjs-backend-wasm';
 export * from '@tensorflow/tfjs-backend-webgl';
 export * from '@tensorflow/tfjs-backend-webgpu';
 export * from '@tensorflow/tfjs-node';
 export * from '@tensorflow/tfjs-node-gpu';
--- a/dist/tfjs.esm.js
+++ b/dist/tfjs.esm.js
--- a/dist/tfjs.version.js
+++ b/dist/tfjs.version.js
@ -4,41 +4,4 @@
  author: <https://github.com/vladmandic>'
 */
-// node_modules/.pnpm/@tensorflow+tfjs@3.13.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs/package.json
+var e="4.22.0";var s="4.22.0";var t="4.22.0";var n="4.22.0";var r="4.22.0";var i="4.22.0";var h={tfjs:e,"tfjs-core":e,"tfjs-converter":s,"tfjs-backend-cpu":t,"tfjs-backend-webgl":n,"tfjs-backend-wasm":r,"tfjs-backend-webgpu":i};export{h as version};
 var version = "3.13.0";
 // node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/package.json
 var version2 = "3.13.0";
 // node_modules/.pnpm/@tensorflow+tfjs-data@3.13.0_dadde02861a8b00ace7633d17571891e/node_modules/@tensorflow/tfjs-data/package.json
 var version3 = "3.13.0";
 // node_modules/.pnpm/@tensorflow+tfjs-layers@3.13.0_@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-layers/package.json
 var version4 = "3.13.0";
 // node_modules/.pnpm/@tensorflow+tfjs-converter@3.13.0_@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-converter/package.json
 var version5 = "3.13.0";
 // node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.13.0_@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-backend-cpu/package.json
 var version6 = "3.13.0";
 // node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.13.0_@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-backend-webgl/package.json
 var version7 = "3.13.0";
 // node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.13.0_@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-backend-wasm/package.json
 var version8 = "3.13.0";
 // tfjs/tf-version.ts
 var version9 = {
  tfjs: version,
  "tfjs-core": version2,
  "tfjs-data": version3,
  "tfjs-layers": version4,
  "tfjs-converter": version5,
  "tfjs-backend-cpu": version6,
  "tfjs-backend-webgl": version7,
  "tfjs-backend-wasm": version8
 };
 export {
  version9 as version
 };
--- a/human.service
+++ b/human.service
@ -5,7 +5,7 @@ After=network.target network-online.target
 [Service]
 Type=simple
 Environment="NODE_ENV=production"
-ExecStart=<path-to-node> <your-project-folder>/node_modules/@vladmandic/build/src/build.js --profile development
+ExecStart=<path-to-node> <your-project-folder>/node_modules/@vladmandic/build/src/build.js --profile serve
 WorkingDirectory=<your-project-folder>
 StandardOutput=inherit
 StandardError=inherit
--- a/models/README.md
+++ b/models/README.md
@ -3,308 +3,3 @@
 For details see Wiki:
 - [**List of Models & Credits**](https://github.com/vladmandic/human/wiki/Models)
 ## Model signatures:
 ```js
 INFO:  graph model: /home/vlado/dev/human/models/iris.json
 INFO:  created on: 2020-10-12T18:46:47.060Z
 INFO:  metadata: { generatedBy: 'https://github.com/google/mediapipe', convertedBy: 'https://github.com/vladmandic', version: undefined }
 INFO:  model inputs based on signature
 { name: 'input_1:0', dtype: 'DT_FLOAT', shape: [ -1, 64, 64, 3 ] }
 INFO:  model outputs based on signature
 { id: 0, name: 'Identity:0', dytpe: 'DT_FLOAT', shape: [ -1, 1, 1, 228 ] }
 INFO:  tensors: 191
 DATA:  weights: {
  files: [ 'iris.bin' ],
  size: { disk: 2599092, memory: 2599092 },
  count: { total: 191, float32: 189, int32: 2 },
  quantized: { none: 191 },
  values: { total: 649773, float32: 649764, int32: 9 }
 }
 DATA:  kernel ops: {
  graph: [ 'Const', 'Placeholder', 'Identity' ],
  convolution: [ '_FusedConv2D', 'DepthwiseConv2dNative', 'MaxPool' ],
  arithmetic: [ 'AddV2' ],
  basic_math: [ 'Prelu' ],
  transformation: [ 'Pad' ],
  slice_join: [ 'ConcatV2' ]
 }
 INFO:  graph model: /home/vlado/dev/human/models/facemesh.json
 INFO:  created on: 2020-10-12T18:46:46.944Z
 INFO:  metadata: { generatedBy: 'https://github.com/google/mediapipe', convertedBy: 'https://github.com/vladmandic', version: undefined }
 INFO:  model inputs based on signature
 { name: 'input_1:0', dtype: 'DT_FLOAT', shape: [ 1, 192, 192, 3 ] }
 INFO:  model outputs based on signature
 { id: 0, name: 'Identity_1:0', dytpe: 'DT_FLOAT', shape: [ 1, 266 ] }
 { id: 1, name: 'Identity_2:0', dytpe: 'DT_FLOAT', shape: [ 1, 1 ] }
 { id: 2, name: 'Identity:0', dytpe: 'DT_FLOAT', shape: [ 1, 1404 ] }
 INFO:  tensors: 118
 DATA:  weights: {
  files: [ 'facemesh.bin' ],
  size: { disk: 2955780, memory: 2955780 },
  count: { total: 118, float32: 114, int32: 4 },
  quantized: { none: 118 },
  values: { total: 738945, float32: 738919, int32: 26 }
 }
 DATA:  kernel ops: {
  graph: [ 'Placeholder', 'Const', 'NoOp', 'Identity' ],
  convolution: [ '_FusedConv2D', 'DepthwiseConv2dNative', 'MaxPool' ],
  arithmetic: [ 'AddV2' ],
  basic_math: [ 'Prelu', 'Sigmoid' ],
  transformation: [ 'Pad', 'Reshape' ]
 }
 INFO:  graph model: /home/vlado/dev/human/models/emotion.json
 INFO:  created on: 2020-11-05T20:11:29.740Z
 INFO:  metadata: { generatedBy: 'https://github.com/oarriaga/face_classification', convertedBy: 'https://github.com/vladmandic', version: undefined }
 INFO:  model inputs based on signature
 { name: 'input_1:0', dtype: 'DT_FLOAT', shape: [ -1, 64, 64, 1 ] }
 INFO:  model outputs based on signature
 { id: 0, name: 'Identity:0', dytpe: 'DT_FLOAT', shape: [ -1, 7 ] }
 INFO:  tensors: 23
 DATA:  weights: {
  files: [ 'emotion.bin' ],
  size: { disk: 820516, memory: 820516 },
  count: { total: 23, float32: 22, int32: 1 },
  quantized: { none: 23 },
  values: { total: 205129, float32: 205127, int32: 2 }
 }
 DATA:  kernel ops: {
  graph: [ 'Const', 'Placeholder', 'Identity' ],
  convolution: [ '_FusedConv2D', 'DepthwiseConv2dNative', 'MaxPool' ],
  arithmetic: [ 'AddV2' ],
  basic_math: [ 'Relu' ],
  reduction: [ 'Mean' ],
  normalization: [ 'Softmax' ]
 }
 INFO:  graph model: /home/vlado/dev/human/models/faceres.json
 INFO:  created on: 2021-03-21T14:12:59.863Z
 INFO:  metadata: { generatedBy: 'https://github.com/HSE-asavchenko/HSE_FaceRec_tf', convertedBy: 'https://github.com/vladmandic', version: undefined }
 INFO:  model inputs based on signature
 { name: 'input_1', dtype: 'DT_FLOAT', shape: [ -1, 224, 224, 3 ] }
 INFO:  model outputs based on signature
 { id: 0, name: 'gender_pred/Sigmoid:0', dytpe: 'DT_FLOAT', shape: [ 1, 1 ] }
 { id: 1, name: 'global_pooling/Mean', dytpe: 'DT_FLOAT', shape: [ 1, 1024 ] }
 { id: 2, name: 'age_pred/Softmax:0', dytpe: 'DT_FLOAT', shape: [ 1, 100 ] }
 INFO:  tensors: 128
 DATA:  weights: {
  files: [ 'faceres.bin' ],
  size: { disk: 6978814, memory: 13957620 },
  count: { total: 128, float32: 127, int32: 1 },
  quantized: { float16: 127, none: 1 },
  values: { total: 3489405, float32: 3489403, int32: 2 }
 }
 DATA:  kernel ops: {
  graph: [ 'Const', 'Placeholder' ],
  convolution: [ 'Conv2D', 'DepthwiseConv2dNative' ],
  arithmetic: [ 'Add', 'Minimum', 'Maximum', 'Mul' ],
  basic_math: [ 'Relu', 'Sigmoid' ],
  reduction: [ 'Mean' ],
  matrices: [ '_FusedMatMul' ],
  normalization: [ 'Softmax' ]
 }
 INFO:  graph model: /home/vlado/dev/human/models/blazeface.json
 INFO:  created on: 2020-10-15T19:57:26.419Z
 INFO:  metadata: { generatedBy: 'https://github.com/google/mediapipe', convertedBy: 'https://github.com/vladmandic', version: undefined }
 INFO:  model inputs based on signature
 { name: 'input:0', dtype: 'DT_FLOAT', shape: [ 1, 256, 256, 3 ] }
 INFO:  model outputs based on signature
 { id: 0, name: 'Identity_3:0', dytpe: 'DT_FLOAT', shape: [ 1, 384, 16 ] }
 { id: 1, name: 'Identity:0', dytpe: 'DT_FLOAT', shape: [ 1, 512, 1 ] }
 { id: 2, name: 'Identity_1:0', dytpe: 'DT_FLOAT', shape: [ 1, 384, 1 ] }
 { id: 3, name: 'Identity_2:0', dytpe: 'DT_FLOAT', shape: [ 1, 512, 16 ] }
 INFO:  tensors: 112
 DATA:  weights: {
  files: [ 'blazeface.bin' ],
  size: { disk: 538928, memory: 538928 },
  count: { total: 112, float32: 106, int32: 6 },
  quantized: { none: 112 },
  values: { total: 134732, float32: 134704, int32: 28 }
 }
 DATA:  kernel ops: {
  graph: [ 'Const', 'Placeholder', 'Identity' ],
  convolution: [ '_FusedConv2D', 'DepthwiseConv2dNative', 'MaxPool' ],
  arithmetic: [ 'AddV2' ],
  basic_math: [ 'Relu' ],
  transformation: [ 'Pad', 'Reshape' ]
 }
 INFO:  graph model: /home/vlado/dev/human/models/mb3-centernet.json
 INFO:  created on: 2021-05-19T11:50:13.013Z
 INFO:  metadata: { generatedBy: 'https://github.com/610265158/mobilenetv3_centernet', convertedBy: 'https://github.com/vladmandic', version: undefined }
 INFO:  model inputs based on signature
 { name: 'tower_0/images', dtype: 'DT_FLOAT', shape: [ 1, 512, 512, 3 ] }
 INFO:  model outputs based on signature
 { id: 0, name: 'tower_0/wh', dytpe: 'DT_FLOAT', shape: [ 1, 128, 128, 4 ] }
 { id: 1, name: 'tower_0/keypoints', dytpe: 'DT_FLOAT', shape: [ 1, 128, 128, 80 ] }
 { id: 2, name: 'tower_0/detections', dytpe: 'DT_FLOAT', shape: [ 1, 100, 6 ] }
 INFO:  tensors: 267
 DATA:  weights: {
  files: [ 'mb3-centernet.bin' ],
  size: { disk: 4030290, memory: 8060260 },
  count: { total: 267, float32: 227, int32: 40 },
  quantized: { float16: 227, none: 40 },
  values: { total: 2015065, float32: 2014985, int32: 80 }
 }
 DATA:  kernel ops: {
  graph: [ 'Const', 'Placeholder', 'Identity' ],
  convolution: [ '_FusedConv2D', 'FusedDepthwiseConv2dNative', 'DepthwiseConv2dNative', 'Conv2D', 'MaxPool' ],
  arithmetic: [ 'Mul', 'Add', 'FloorDiv', 'FloorMod', 'Sub' ],
  basic_math: [ 'Relu6', 'Relu', 'Sigmoid' ],
  reduction: [ 'Mean' ],
  image: [ 'ResizeBilinear' ],
  slice_join: [ 'ConcatV2', 'GatherV2', 'StridedSlice' ],
  transformation: [ 'Reshape', 'Cast', 'ExpandDims' ],
  logical: [ 'Equal' ],
  evaluation: [ 'TopKV2' ]
 }
 INFO:  graph model: /home/vlado/dev/human/models/movenet-lightning.json
 INFO:  created on: 2021-05-29T12:26:32.994Z
 INFO:  metadata: { generatedBy: 'https://tfhub.dev/google/movenet/singlepose/lightning/4', convertedBy: 'https://github.com/vladmandic', version: undefined }
 INFO:  model inputs based on signature
 { name: 'input:0', dtype: 'DT_INT32', shape: [ 1, 192, 192, 3 ] }
 INFO:  model outputs based on signature
 { id: 0, name: 'Identity:0', dytpe: 'DT_FLOAT', shape: [ 1, 1, 17, 3 ] }
 INFO:  tensors: 180
 DATA:  weights: {
  files: [ 'movenet-lightning.bin' ],
  size: { disk: 4650216, memory: 9300008 },
  count: { total: 180, int32: 31, float32: 149 },
  quantized: { none: 31, float16: 149 },
  values: { total: 2325002, int32: 106, float32: 2324896 }
 }
 DATA:  kernel ops: {
  graph: [ 'Const', 'Placeholder', 'Identity' ],
  transformation: [ 'Cast', 'ExpandDims', 'Squeeze', 'Reshape' ],
  slice_join: [ 'Unpack', 'Pack', 'GatherNd', 'ConcatV2' ],
  arithmetic: [ 'Sub', 'Mul', 'AddV2', 'FloorDiv', 'SquaredDifference', 'RealDiv' ],
  convolution: [ '_FusedConv2D', 'FusedDepthwiseConv2dNative', 'DepthwiseConv2dNative' ],
  image: [ 'ResizeBilinear' ],
  basic_math: [ 'Sigmoid', 'Sqrt' ],
  reduction: [ 'ArgMax' ]
 }
 INFO:  graph model: /home/vlado/dev/human/models/selfie.json
 INFO:  created on: 2021-06-04T13:46:56.904Z
 INFO:  metadata: { generatedBy: 'https://github.com/PINTO0309/PINTO_model_zoo/tree/main/109_Selfie_Segmentation', convertedBy: 'https://github.com/vladmandic', version: '561.undefined' }
 INFO:  model inputs based on signature
 { name: 'input_1:0', dtype: 'DT_FLOAT', shape: [ 1, 256, 256, 3 ] }
 INFO:  model outputs based on signature
 { id: 0, name: 'activation_10:0', dytpe: 'DT_FLOAT', shape: [ 1, 256, 256, 1 ] }
 INFO:  tensors: 136
 DATA:  weights: {
  files: [ 'selfie.bin' ],
  size: { disk: 212886, memory: 425732 },
  count: { total: 136, int32: 4, float32: 132 },
  quantized: { none: 4, float16: 132 },
  values: { total: 106433, int32: 10, float32: 106423 }
 }
 DATA:  kernel ops: {
  graph: [ 'Const', 'Placeholder' ],
  convolution: [ 'Conv2D', 'DepthwiseConv2dNative', 'AvgPool', 'Conv2DBackpropInput' ],
  arithmetic: [ 'Add', 'Mul', 'AddV2', 'AddN' ],
  basic_math: [ 'Relu6', 'Relu', 'Sigmoid' ],
  image: [ 'ResizeBilinear' ]
 }
 INFO:  graph model: /home/vlado/dev/human/models/handtrack.json
 INFO:  created on: 2021-09-21T12:09:47.583Z
 INFO:  metadata: { generatedBy: 'https://github.com/victordibia/handtracking', convertedBy: 'https://github.com/vladmandic', version: '561.undefined' }
 INFO:  model inputs based on signature
 { name: 'input_tensor:0', dtype: 'DT_UINT8', shape: [ 1, 320, 320, 3 ] }
 INFO:  model outputs based on signature
 { id: 0, name: 'Identity_2:0', dytpe: 'DT_FLOAT', shape: [ 1, 100 ] }
 { id: 1, name: 'Identity_4:0', dytpe: 'DT_FLOAT', shape: [ 1, 100 ] }
 { id: 2, name: 'Identity_6:0', dytpe: 'DT_FLOAT', shape: [ 1, 12804, 4 ] }
 { id: 3, name: 'Identity_1:0', dytpe: 'DT_FLOAT', shape: [ 1, 100, 4 ] }
 { id: 4, name: 'Identity_3:0', dytpe: 'DT_FLOAT', shape: [ 1, 100, 8 ] }
 { id: 5, name: 'Identity_5:0', dytpe: 'DT_FLOAT', shape: [ 1 ] }
 { id: 6, name: 'Identity:0', dytpe: 'DT_FLOAT', shape: [ 1, 100 ] }
 { id: 7, name: 'Identity_7:0', dytpe: 'DT_FLOAT', shape: [ 1, 12804, 8 ] }
 INFO:  tensors: 619
 DATA:  weights: {
  files: [ 'handtrack.bin' ],
  size: { disk: 2964837, memory: 11846016 },
  count: { total: 619, int32: 347, float32: 272 },
  quantized: { none: 347, uint8: 272 },
  values: { total: 2961504, int32: 1111, float32: 2960393 }
 }
 DATA:  kernel ops: {
  graph: [ 'Const', 'Placeholder', 'Identity', 'Shape', 'NoOp' ],
  control: [ 'TensorListReserve', 'Enter', 'TensorListFromTensor', 'Merge', 'LoopCond', 'Switch', 'Exit', 'TensorListStack', 'NextIteration', 'TensorListSetItem', 'TensorListGetItem' ],
  logical: [ 'Less', 'LogicalAnd', 'Select', 'Greater', 'GreaterEqual' ],
  convolution: [ '_FusedConv2D', 'FusedDepthwiseConv2dNative', 'DepthwiseConv2dNative' ],
  arithmetic: [ 'AddV2', 'Mul', 'Sub', 'Minimum', 'Maximum' ],
  transformation: [ 'Cast', 'ExpandDims', 'Squeeze', 'Reshape', 'Pad' ],
  slice_join: [ 'Unpack', 'StridedSlice', 'Pack', 'ConcatV2', 'Slice', 'GatherV2', 'Split' ],
  image: [ 'ResizeBilinear' ],
  basic_math: [ 'Reciprocal', 'Sigmoid', 'Exp' ],
  matrices: [ 'Transpose' ],
  dynamic: [ 'NonMaxSuppressionV5', 'Where' ],
  creation: [ 'Fill', 'Range' ],
  evaluation: [ 'TopKV2' ],
  reduction: [ 'Sum' ]
 }
 INFO:  graph model: /home/vlado/dev/human/models/antispoof.json
 INFO:  created on: 2021-10-13T14:20:27.100Z
 INFO:  metadata: { generatedBy: 'https://www.kaggle.com/anku420/fake-face-detection', convertedBy: 'https://github.com/vladmandic', version: '716.undefined' }
 INFO:  model inputs based on signature
 { name: 'conv2d_input', dtype: 'DT_FLOAT', shape: [ -1, 128, 128, 3 ] }
 INFO:  model outputs based on signature
 { id: 0, name: 'activation_4', dytpe: 'DT_FLOAT', shape: [ -1, 1 ] }
 INFO:  tensors: 11
 DATA:  weights: {
  files: [ 'antispoof.bin' ],
  size: { disk: 853098, memory: 1706188 },
  count: { total: 11, float32: 10, int32: 1 },
  quantized: { float16: 10, none: 1 },
  values: { total: 426547, float32: 426545, int32: 2 }
 }
 DATA:  kernel ops: { graph: [ 'Const', 'Placeholder', 'Identity' ], convolution: [ '_FusedConv2D', 'MaxPool' ], basic_math: [ 'Relu', 'Sigmoid' ], transformation: [ 'Reshape' ], matrices: [ '_FusedMatMul' ] }
 INFO:  graph model: /home/vlado/dev/human/models/handlandmark-full.json
 INFO:  created on: 2021-10-31T12:27:49.343Z
 INFO:  metadata: { generatedBy: 'https://github.com/google/mediapipe', convertedBy: 'https://github.com/vladmandic', version: '808.undefined' }
 INFO:  model inputs based on signature
 { name: 'input_1', dtype: 'DT_FLOAT', shape: [ 1, 224, 224, 3 ] }
 INFO:  model outputs based on signature
 { id: 0, name: 'Identity_3:0', dytpe: 'DT_FLOAT', shape: [ 1, 63 ] }
 { id: 1, name: 'Identity:0', dytpe: 'DT_FLOAT', shape: [ 1, 63 ] }
 { id: 2, name: 'Identity_1:0', dytpe: 'DT_FLOAT', shape: [ 1, 1 ] }
 { id: 3, name: 'Identity_2:0', dytpe: 'DT_FLOAT', shape: [ 1, 1 ] }
 INFO:  tensors: 103
 DATA:  weights: {
  files: [ 'handlandmark-full.bin' ],
  size: { disk: 5431368, memory: 10862728 },
  count: { total: 103, float32: 102, int32: 1 },
  quantized: { float16: 102, none: 1 },
  values: { total: 2715682, float32: 2715680, int32: 2 }
 }
 DATA:  kernel ops: {
  graph: [ 'Const', 'Placeholder', 'Identity' ],
  convolution: [ 'Conv2D', 'DepthwiseConv2dNative' ],
  arithmetic: [ 'AddV2', 'AddN' ],
  basic_math: [ 'Relu6', 'Sigmoid' ],
  reduction: [ 'Mean' ],
  matrices: [ '_FusedMatMul' ]
 }
 INFO:  graph model: /home/vlado/dev/human/models/liveness.json
 INFO:  created on: 2021-11-09T12:39:11.760Z
 INFO:  metadata: { generatedBy: 'https://github.com/leokwu/livenessnet', convertedBy: 'https://github.com/vladmandic', version: '808.undefined' }
 INFO:  model inputs based on signature
 { name: 'conv2d_1_input', dtype: 'DT_FLOAT', shape: [ -1, 32, 32, 3 ] }
 INFO:  model outputs based on signature
 { id: 0, name: 'activation_6', dytpe: 'DT_FLOAT', shape: [ -1, 2 ] }
 INFO:  tensors: 23
 DATA:  weights: {
  files: [ 'liveness.bin' ],
  size: { disk: 592976, memory: 592976 },
  count: { total: 23, float32: 22, int32: 1 },
  quantized: { none: 23 },
  values: { total: 148244, float32: 148242, int32: 2 }
 }
 DATA:  kernel ops: {
  graph: [ 'Const', 'Placeholder', 'Identity' ],
  convolution: [ '_FusedConv2D', 'MaxPool' ],
  arithmetic: [ 'Mul', 'Add', 'AddV2' ],
  transformation: [ 'Reshape' ],
  matrices: [ '_FusedMatMul' ],
  normalization: [ 'Softmax' ]
 }
 ```
--- a/models/mb3-centernet.bin
+++ b/models/mb3-centernet.bin
--- a/models/mb3-centernet.json
+++ b/models/mb3-centernet.json
--- a/models/facemesh.bin
+++ b/models/facemesh.bin
--- a/models/facemesh.json
+++ b/models/facemesh.json
--- a/models/handlandmark-full.bin
+++ b/models/handlandmark-full.bin
--- a/models/handlandmark-lite.bin
+++ b/models/handlandmark-lite.bin
--- a/models/handlandmark-lite.json
+++ b/models/handlandmark-lite.json
--- a/models/models.json
+++ b/models/models.json
@ -0,0 +1,60 @@
 {
  "antispoof": 853098,
  "blazeface": 538928,
  "centernet": 4030290,
  "emotion": 820516,
  "facemesh": 1477958,
  "faceres": 6978814,
  "handlandmark-lite": 2023432,
  "handtrack": 2964837,
  "iris": 2599092,
  "liveness": 592976,
  "models": 0,
  "movenet-lightning": 4650216,
  "affectnet-mobilenet": 6920630,
  "age": 161240,
  "blazeface-back": 538928,
  "blazeface-front": 402048,
  "blazepose-detector": 5928856,
  "blazepose-full": 6339202,
  "blazepose-heavy": 27502466,
  "blazepose-lite": 2726402,
  "efficientpose": 5651240,
  "faceboxes": 2013002,
  "facemesh-attention-pinto": 2387598,
  "facemesh-attention": 2382414,
  "facemesh-detection-full": 1026192,
  "facemesh-detection-short": 201268,
  "faceres-deep": 13957620,
  "gear-e1": 112438,
  "gear-e2": 112438,
  "gear": 1498916,
  "gender-ssrnet-imdb": 161236,
  "gender": 201808,
  "handdetect": 3515612,
  "handlandmark-full": 5431368,
  "handlandmark-sparse": 5286322,
  "handskeleton": 5502280,
  "meet": 372228,
  "mobileface": 2183192,
  "mobilefacenet": 5171976,
  "movenet-multipose": 9448838,
  "movenet-thunder": 12477112,
  "nanodet": 7574558,
  "posenet": 5032780,
  "rvm": 3739355,
  "selfie": 212886,
  "anti-spoofing": 853098,
  "efficientpose-i-lite": 2269064,
  "efficientpose-ii-lite": 5651240,
  "efficientpose-iv": 25643252,
  "insightface-efficientnet-b0": 13013224,
  "insightface-ghostnet-strides1": 8093408,
  "insightface-ghostnet-strides2": 8049584,
  "insightface-mobilenet-emore": 6938536,
  "insightface-mobilenet-swish": 12168584,
  "nanodet-e": 12319156,
  "nanodet-g": 7574558,
  "nanodet-m": 1887474,
  "nanodet-t": 5294216
 }
--- a/models/selfie.bin
+++ b/models/selfie.bin
--- a/Show More
+++ b/Show More