diff --git a/CHANGELOG.md b/CHANGELOG.md
index 504ef673..c725042a 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # @vladmandic/human  
 
-Version: **1.9.4**  
+Version: **2.0.0**  
 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: **Vladimir Mandic <mandic00@live.com>**  
@@ -9,8 +9,9 @@ Repository: **<git+https://github.com/vladmandic/human.git>**
 
 ## Changelog
 
-### **HEAD -> main** 2021/05/29 mandic00@live.com
+### **HEAD -> main** 2021/05/30 mandic00@live.com
 
+- quantize handdetect model
 - added experimental movenet-lightning and removed blazepose from default dist
 - added experimental face.rotation.gaze
 - fix and optimize for mobile platform
diff --git a/README.md b/README.md
index a76dbaa3..a369c4ac 100644
--- a/README.md
+++ b/README.md
@@ -66,6 +66,7 @@ Check out [**Live Demo**](https://vladmandic.github.io/human/demo/index.html) fo
 - [**Platform Support**](https://github.com/vladmandic/human/wiki/Platforms)
 - [**List of Models & Credits**](https://github.com/vladmandic/human/wiki/Models)
 - [**Security & Privacy Policy**](https://github.com/vladmandic/human/blob/main/SECURITY.md)
+- [**License & Usage Restrictions**](https://github.com/vladmandic/human/blob/main/LICENSE)
 
 <br>
 
@@ -77,8 +78,8 @@ Check out [**Live Demo**](https://vladmandic.github.io/human/demo/index.html) fo
 
 ## Options
 
-As presented in the demo application...  
-> [demo/index.html](demo/index.html)
+All options as presented in the demo application...  
+> [demo/index.html](demo/index.html)  
 
 ![Options visible in demo](assets/screenshot-menu.png)
 
@@ -88,7 +89,7 @@ As presented in the demo application...
 
 <br>
 
-**Training image:**  
+**Validation image:**  
 > [demo/index.html](demo/index.html?image=%22../assets/human-sample-upper.jpg%22)
 
 ![Example Training Image](assets/screenshot-sample.png)
@@ -104,7 +105,10 @@ As presented in the demo application...
 ![Example Using WebCam](assets/screenshot-webcam.jpg)
 
 **Face Similarity Matching:**  
-> [demo/facematch.html](demo/facematch.html)
+Extracts all faces from provided input images,  
+sorts them by similarity to selected face  
+and optionally matches detected face with database of known people to guess their names
+> [demo/facematch.html](demo/facematch.html)  
 
 ![Face Matching](assets/screenshot-facematch.jpg)
 
diff --git a/TODO.md b/TODO.md
index 5cd873f8..dc44c252 100644
--- a/TODO.md
+++ b/TODO.md
@@ -7,7 +7,7 @@ N/A
 ## Exploring Features
 
 - Implement demo as installable PWA with model caching
-- Implement results interpolation on library level
+- Implement results interpolation on library level instead inside draw functions
 - Switch to TypeScript 4.3
 
 ## Explore Models
@@ -16,6 +16,14 @@ N/A
 
 ## In Progress
 
-- Face interpolation
-- Gaze interpolation
+- Face rotation interpolation
+- Object detection interpolation
 - Unify score/confidence variables
+
+## Issues
+
+- CenterNet WebGL: <https://github.com/tensorflow/tfjs/issues/5145>
+- CenterNet WASM: <https://github.com/tensorflow/tfjs/issues/5110>
+- NanoDet WASM: <https://github.com/tensorflow/tfjs/issues/4824>
+- BlazeFace and HandPose rotation in NodeJS: <https://github.com/tensorflow/tfjs/issues/4066>
+- TypeDoc with TS 4.3: <https://github.com/TypeStrong/typedoc/issues/1589>
diff --git a/demo/index-worker.js b/demo/index-worker.js
index b1d5a434..88e17230 100644
--- a/demo/index-worker.js
+++ b/demo/index-worker.js
@@ -13,6 +13,7 @@ function log(...msg) {
 onmessage = async (msg) => {
   if (busy) return;
   busy = true;
+  // received from index.js using:
   // worker.postMessage({ image: image.data.buffer, width: canvas.width, height: canvas.height, config }, [image.data.buffer]);
   const image = new ImageData(new Uint8ClampedArray(msg.data.image), msg.data.width, msg.data.height);
   let result = {};
diff --git a/demo/index.js b/demo/index.js
index 23ede44b..b62e7521 100644
--- a/demo/index.js
+++ b/demo/index.js
@@ -1,13 +1,26 @@
-// @ts-nocheck // typescript checks disabled as this is pure javascript
-
 /**
  * Human demo for browsers
  *
- * Main demo app that exposes all Human functionality
+ * @description Main demo app that exposes all Human functionality
+ *
+ * @params Optional URL parameters:
+ * image=<imagePath:string>: perform detection on specific image and finish
+ * worker=<true|false>: use WebWorkers
+ * backend=<webgl|wasm|cpu>: use specific TF backend for operations
+ * preload=<true|false>: pre-load all configured models
+ * warmup=<true|false>: warmup all configured models
+ *
+ * @example <https://wyse:10031/?backend=wasm&worker=true&image="/assets/sample-me.jpg">
+ *
+ * @configuration
+ * userConfig={}: contains all model configuration used by human
+ * drawOptions={}: contains all draw variables used by human.draw
+ * ui={}: contains all variables exposed in the UI
  */
 
+// @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-nobundle.js'; // this requires that tf is loaded manually and bundled before human can be used
 import Menu from './helpers/menu.js';
 import GLBench from './helpers/gl-bench.js';
 import webRTC from './helpers/webrtc.js';
@@ -15,8 +28,7 @@ import webRTC from './helpers/webrtc.js';
 let human;
 
 const userConfig = {
-  warmup: 'full',
-  /*
+  warmup: 'none',
   backend: 'webgl',
   async: false,
   cacheSensitivity: 0,
@@ -34,10 +46,9 @@ const userConfig = {
   hand: { enabled: false },
   // body: { enabled: true, modelPath: 'posenet.json' },
   // body: { enabled: true, modelPath: 'blazepose.json' },
-  body: { enabled: true, modelPath: 'movenet-lightning.json' },
-  object: { enabled: false },
+  body: { enabled: false, modelPath: 'movenet-lightning.json' },
+  object: { enabled: true },
   gesture: { enabled: true },
-  */
 };
 
 const drawOptions = {
@@ -53,7 +64,7 @@ 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: false, // use web workers for processing
+  useWorker: true, // use web workers for processing
   worker: 'index-worker.js',
   maxFPSframes: 10, // keep fps history for how many frames
   modelsPreload: true, // preload human models on startup
@@ -84,6 +95,7 @@ const ui = {
 
   // sample images
   compare: '../assets/sample-me.jpg', // base image for face compare
+  /*
   samples: [
     '../assets/sample6.jpg',
     '../assets/sample1.jpg',
@@ -92,45 +104,10 @@ const ui = {
     '../assets/sample3.jpg',
     '../assets/sample2.jpg',
   ],
-  /*
-  ui.samples = [
-    '../private/daz3d/daz3d-brianna.jpg',
-    '../private/daz3d/daz3d-chiyo.jpg',
-    '../private/daz3d/daz3d-cody.jpg',
-    '../private/daz3d/daz3d-drew-01.jpg',
-    '../private/daz3d/daz3d-drew-02.jpg',
-    '../private/daz3d/daz3d-ella-01.jpg',
-    '../private/daz3d/daz3d-ella-02.jpg',
-    '../private/daz3d/daz3d-_emotions01.jpg',
-    '../private/daz3d/daz3d-_emotions02.jpg',
-    '../private/daz3d/daz3d-_emotions03.jpg',
-    '../private/daz3d/daz3d-_emotions04.jpg',
-    '../private/daz3d/daz3d-_emotions05.jpg',
-    '../private/daz3d/daz3d-gillian.jpg',
-    '../private/daz3d/daz3d-ginnifer.jpg',
-    '../private/daz3d/daz3d-hye-01.jpg',
-    '../private/daz3d/daz3d-hye-02.jpg',
-    '../private/daz3d/daz3d-kaia.jpg',
-    '../private/daz3d/daz3d-karen.jpg',
-    '../private/daz3d/daz3d-kiaria-01.jpg',
-    '../private/daz3d/daz3d-kiaria-02.jpg',
-    '../private/daz3d/daz3d-lilah-01.jpg',
-    '../private/daz3d/daz3d-lilah-02.jpg',
-    '../private/daz3d/daz3d-lilah-03.jpg',
-    '../private/daz3d/daz3d-lila.jpg',
-    '../private/daz3d/daz3d-lindsey.jpg',
-    '../private/daz3d/daz3d-megah.jpg',
-    '../private/daz3d/daz3d-selina-01.jpg',
-    '../private/daz3d/daz3d-selina-02.jpg',
-    '../private/daz3d/daz3d-snow.jpg',
-    '../private/daz3d/daz3d-sunshine.jpg',
-    '../private/daz3d/daz3d-taia.jpg',
-    '../private/daz3d/daz3d-tuesday-01.jpg',
-    '../private/daz3d/daz3d-tuesday-02.jpg',
-    '../private/daz3d/daz3d-tuesday-03.jpg',
-    '../private/daz3d/daz3d-zoe.jpg',
-  ];
   */
+  samples: [
+    '../private/daz3d/daz3d-kiaria-02.jpg',
+  ],
 };
 
 // global variables
@@ -267,9 +244,9 @@ async function drawResults(input) {
   // if buffered, immediate loop but limit frame rate although it's going to run slower as JS is singlethreaded
   if (ui.buffered) {
     ui.drawThread = requestAnimationFrame(() => drawResults(input, canvas));
-  } else if (!ui.buffered && ui.drawThread) {
+  } else {
     log('stopping buffered refresh');
-    cancelAnimationFrame(ui.drawThread);
+    if (ui.drawThread) cancelAnimationFrame(ui.drawThread);
     ui.drawThread = null;
   }
 }
@@ -435,7 +412,7 @@ function runHumanDetect(input, canvas, timestamp) {
     offscreen.width = canvas.width;
     offscreen.height = canvas.height;
     const ctx = offscreen.getContext('2d');
-    ctx.drawImage(input, 0, 0, input.width, input.height, 0, 0, canvas.width, canvas.height);
+    ctx.drawImage(input, 0, 0, canvas.width, canvas.height);
     const data = ctx.getImageData(0, 0, canvas.width, canvas.height);
     // perform detection in worker
     webWorker(input, data, canvas, userConfig, timestamp);
@@ -522,6 +499,7 @@ async function detectVideo() {
 
 // just initialize everything and call main function
 async function detectSampleImages() {
+  document.getElementById('play').style.display = 'none';
   document.getElementById('canvas').style.display = 'none';
   document.getElementById('samples-container').style.display = 'block';
   log('running detection of sample images');
@@ -530,6 +508,9 @@ async function detectSampleImages() {
   for (const m of Object.values(menu)) m.hide();
   for (const image of ui.samples) await processImage(image);
   status();
+  document.getElementById('play').style.display = 'none';
+  document.getElementById('loader').style.display = 'none';
+  if (ui.detectThread) cancelAnimationFrame(ui.detectThread);
 }
 
 function setupMenu() {
@@ -692,6 +673,12 @@ async function main() {
 
   document.documentElement.style.setProperty('--icon-size', ui.iconSize);
 
+  // sanity check for webworker compatibility
+  if (typeof Worker === 'undefined' || typeof OffscreenCanvas === 'undefined') {
+    ui.useWorker = false;
+    log('workers are disabled due to missing browser functionality');
+  }
+
   // parse url search params
   const params = new URLSearchParams(location.search);
   log('url options:', params.toString());
diff --git a/demo/node.js b/demo/node.js
index eaf195c3..61aaf9e3 100644
--- a/demo/node.js
+++ b/demo/node.js
@@ -4,6 +4,7 @@
 
 const log = require('@vladmandic/pilogger');
 const fs = require('fs');
+const path = require('path');
 const process = require('process');
 const fetch = require('node-fetch').default;
 
@@ -142,6 +143,7 @@ async function detect(input) {
     log.data('  Object: N/A');
   }
 
+  fs.writeFileSync('result.json', JSON.stringify(result, null, 2));
   // print data to console
   if (result) {
     log.data('Persons:');
@@ -182,13 +184,26 @@ async function main() {
   log.header();
   log.info('Current folder:', process.env.PWD);
   await init();
+  const f = process.argv[2];
   if (process.argv.length !== 3) {
-    log.warn('Parameters: <input image> missing');
+    log.warn('Parameters: <input image | folder> missing');
     await test();
-  } else if (!fs.existsSync(process.argv[2]) && !process.argv[2].startsWith('http')) {
+  } else if (!fs.existsSync(f) && !f.startsWith('http')) {
     log.error(`File not found: ${process.argv[2]}`);
   } else {
-    await detect(process.argv[2]);
+    if (fs.existsSync(f)) {
+      const stat = fs.statSync(f);
+      if (stat.isDirectory()) {
+        const dir = fs.readdirSync(f);
+        for (const file of dir) {
+          await detect(path.join(f, file));
+        }
+      } else {
+        await detect(f);
+      }
+    } else {
+      await detect(f);
+    }
   }
 }
 
diff --git a/models/mb3-centernet.bin b/models/mb3-centernet.bin
index 74771713..97be7b6c 100644
Binary files a/models/mb3-centernet.bin and b/models/mb3-centernet.bin differ
diff --git a/models/mb3-centernet.json b/models/mb3-centernet.json
index de8f1ddd..05e1115a 100644
--- a/models/mb3-centernet.json
+++ b/models/mb3-centernet.json
@@ -19,549 +19,549 @@
   {
       "node":
       [
-          {"name":"tower_0/mul_3/y","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}}}},
-          {"name":"tower_0/strided_slice_9/stack","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"3"}]}}},"dtype":{"type":"DT_INT32"}}},
+          {"name":"tower_0/mul_3/y","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}},"dtype":{"type":"DT_FLOAT"}}},
+          {"name":"tower_0/strided_slice_9/stack","op":"Const","attr":{"dtype":{"type":"DT_INT32"},"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"3"}]}}}}},
           {"name":"tower_0/strided_slice_9/stack_1","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"3"}]}}},"dtype":{"type":"DT_INT32"}}},
-          {"name":"tower_0/strided_slice_9/stack_2","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"3"}]}}},"dtype":{"type":"DT_INT32"}}},
-          {"name":"tower_0/mul_4/y","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}}}},
+          {"name":"tower_0/strided_slice_9/stack_2","op":"Const","attr":{"dtype":{"type":"DT_INT32"},"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"3"}]}}}}},
+          {"name":"tower_0/mul_4/y","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}},"dtype":{"type":"DT_FLOAT"}}},
           {"name":"tower_0/strided_slice_10/stack","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"3"}]}}},"dtype":{"type":"DT_INT32"}}},
           {"name":"tower_0/strided_slice_10/stack_1","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"3"}]}}},"dtype":{"type":"DT_INT32"}}},
           {"name":"tower_0/strided_slice_10/stack_2","op":"Const","attr":{"dtype":{"type":"DT_INT32"},"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"3"}]}}}}},
-          {"name":"tower_0/ExpandDims/dim","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{}}},"dtype":{"type":"DT_INT32"}}},
+          {"name":"tower_0/ExpandDims/dim","op":"Const","attr":{"dtype":{"type":"DT_INT32"},"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{}}}}},
           {"name":"tower_0/mul_5/y","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}},"dtype":{"type":"DT_FLOAT"}}},
-          {"name":"tower_0/strided_slice_11/stack","op":"Const","attr":{"dtype":{"type":"DT_INT32"},"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"3"}]}}}}},
-          {"name":"tower_0/strided_slice_11/stack_1","op":"Const","attr":{"dtype":{"type":"DT_INT32"},"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"3"}]}}}}},
-          {"name":"tower_0/strided_slice_11/stack_2","op":"Const","attr":{"dtype":{"type":"DT_INT32"},"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"3"}]}}}}},
+          {"name":"tower_0/strided_slice_11/stack","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"3"}]}}},"dtype":{"type":"DT_INT32"}}},
+          {"name":"tower_0/strided_slice_11/stack_1","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"3"}]}}},"dtype":{"type":"DT_INT32"}}},
+          {"name":"tower_0/strided_slice_11/stack_2","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"3"}]}}},"dtype":{"type":"DT_INT32"}}},
           {"name":"tower_0/ExpandDims_1/dim","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{}}},"dtype":{"type":"DT_INT32"}}},
-          {"name":"tower_0/mul_6/y","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}},"dtype":{"type":"DT_FLOAT"}}},
+          {"name":"tower_0/mul_6/y","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}}}},
           {"name":"tower_0/BatchGather/concat","op":"Const","attr":{"dtype":{"type":"DT_INT32"},"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"2"}]}}}}},
           {"name":"tower_0/strided_slice_6","op":"Const","attr":{"dtype":{"type":"DT_INT32"},"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{}}}}},
           {"name":"tower_0/BatchGather/Reshape_1/shape","op":"Const","attr":{"dtype":{"type":"DT_INT32"},"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"1"}]}}}}},
           {"name":"tower_0/BatchGather/GatherV2/axis","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{}}},"dtype":{"type":"DT_INT32"}}},
           {"name":"tower_0/BatchGather/concat_1","op":"Const","attr":{"dtype":{"type":"DT_INT32"},"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"3"}]}}}}},
-          {"name":"tower_0/strided_slice_12/stack","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"3"}]}}},"dtype":{"type":"DT_INT32"}}},
-          {"name":"tower_0/strided_slice_12/stack_1","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"3"}]}}},"dtype":{"type":"DT_INT32"}}},
+          {"name":"tower_0/strided_slice_12/stack","op":"Const","attr":{"dtype":{"type":"DT_INT32"},"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"3"}]}}}}},
+          {"name":"tower_0/strided_slice_12/stack_1","op":"Const","attr":{"dtype":{"type":"DT_INT32"},"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"3"}]}}}}},
           {"name":"tower_0/strided_slice_12/stack_2","op":"Const","attr":{"dtype":{"type":"DT_INT32"},"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"3"}]}}}}},
           {"name":"tower_0/ExpandDims_3/dim","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{}}},"dtype":{"type":"DT_INT32"}}},
           {"name":"tower_0/Reshape/shape","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"2"}]}}},"dtype":{"type":"DT_INT32"}}},
-          {"name":"tower_0/TopKV2/k","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{}}},"dtype":{"type":"DT_INT32"}}},
-          {"name":"tower_0/strided_slice_7","op":"Const","attr":{"dtype":{"type":"DT_INT32"},"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{}}}}},
-          {"name":"tower_0/ExpandDims_2/dim","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{}}},"dtype":{"type":"DT_INT32"}}},
-          {"name":"tower_0/concat_1/axis","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{}}},"dtype":{"type":"DT_INT32"}}},
+          {"name":"tower_0/TopKV2/k","op":"Const","attr":{"dtype":{"type":"DT_INT32"},"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{}}}}},
+          {"name":"tower_0/strided_slice_7","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{}}},"dtype":{"type":"DT_INT32"}}},
+          {"name":"tower_0/ExpandDims_2/dim","op":"Const","attr":{"dtype":{"type":"DT_INT32"},"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{}}}}},
+          {"name":"tower_0/concat_1/axis","op":"Const","attr":{"dtype":{"type":"DT_INT32"},"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{}}}}},
           {"name":"CenternetHead/centernet_cls_output/depthwise_weights","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"3"},{"size":"3"},{"size":"128"},{"size":"1"}]}}},"dtype":{"type":"DT_FLOAT"}}},
           {"name":"CenternetHead/centernet_cls_output/pointwise_weights","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"1"},{"size":"1"},{"size":"128"},{"size":"80"}]}}},"dtype":{"type":"DT_FLOAT"}}},
           {"name":"CenternetHead/centernet_cls_output/biases","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"80"}]}}}}},
-          {"name":"CenternetHead/c2_reverse/3x3/depthwise_weights","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"5"},{"size":"5"},{"size":"18"},{"size":"1"}]}}},"dtype":{"type":"DT_FLOAT"}}},
+          {"name":"CenternetHead/c2_reverse/3x3/depthwise_weights","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"5"},{"size":"5"},{"size":"18"},{"size":"1"}]}}}}},
           {"name":"CenternetHead/c3_upsample/branch_x_upsample_resize/depthwise_weights","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"3"},{"size":"3"},{"size":"192"},{"size":"1"}]}}}}},
-          {"name":"CenternetHead/c3_reverse/3x3/depthwise_weights","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"5"},{"size":"5"},{"size":"24"},{"size":"1"}]}}},"dtype":{"type":"DT_FLOAT"}}},
-          {"name":"CenternetHead/c4_upsample/branch_x_upsample_resize/depthwise_weights","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"3"},{"size":"3"},{"size":"256"},{"size":"1"}]}}},"dtype":{"type":"DT_FLOAT"}}},
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       }
   ]
 }
\ No newline at end of file
diff --git a/package.json b/package.json
index 9eac6b5b..aae38dbe 100644
--- a/package.json
+++ b/package.json
@@ -1,6 +1,6 @@
 {
   "name": "@vladmandic/human",
-  "version": "1.9.4",
+  "version": "2.0.0",
   "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",
   "sideEffects": false,
   "main": "dist/human.node.js",
@@ -68,10 +68,10 @@
     "canvas": "^2.8.0",
     "chokidar": "^3.5.1",
     "dayjs": "^1.10.5",
-    "esbuild": "^0.12.4",
+    "esbuild": "^0.12.5",
     "eslint": "^7.27.0",
     "eslint-config-airbnb-base": "^14.2.1",
-    "eslint-plugin-import": "^2.23.3",
+    "eslint-plugin-import": "^2.23.4",
     "eslint-plugin-json": "^3.0.0",
     "eslint-plugin-node": "^11.1.0",
     "eslint-plugin-promise": "^5.1.0",
diff --git a/src/draw/draw.ts b/src/draw/draw.ts
index 02e31da6..70d61da3 100644
--- a/src/draw/draw.ts
+++ b/src/draw/draw.ts
@@ -503,17 +503,18 @@ export async function person(inCanvas: HTMLCanvasElement, result: Array<Person>,
 }
 
 function calcBuffered(newResult, localOptions) {
-  // if (newResult.timestamp !== bufferedResult?.timestamp) bufferedResult = JSON.parse(JSON.stringify(newResult)); // no need to force update
-  // each record is only updated using deep copy when number of detected record changes, otherwise it will converge by itself
+  // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself
+  // otherwise bufferedResult is a shallow clone of result plus updated local calculated values
+  // thus mixing by-reference and by-value assignments to minimize memory operations
 
   // interpolate body results
-  if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) bufferedResult.body = JSON.parse(JSON.stringify(newResult.body));
-  for (let i = 0; i < newResult.body.length; i++) { // update body: box, boxRaw, keypoints
-    bufferedResult.body[i].box = newResult.body[i].box
-      .map((box, j) => ((localOptions.bufferedFactor - 1) * bufferedResult.body[i].box[j] + box) / localOptions.bufferedFactor) as [number, number, number, number];
-    bufferedResult.body[i].boxRaw = newResult.body[i].boxRaw
-      .map((box, j) => ((localOptions.bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + box) / localOptions.bufferedFactor) as [number, number, number, number];
-    bufferedResult.body[i].keypoints = newResult.body[i].keypoints
+  if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) bufferedResult.body = JSON.parse(JSON.stringify(newResult.body)); // deep clone once
+  for (let i = 0; i < newResult.body.length; i++) {
+    const box = newResult.body[i].box // update box
+      .map((b, j) => ((localOptions.bufferedFactor - 1) * bufferedResult.body[i].box[j] + b) / localOptions.bufferedFactor) as [number, number, number, number];
+    const boxRaw = newResult.body[i].boxRaw // update boxRaw
+      .map((b, j) => ((localOptions.bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + b) / localOptions.bufferedFactor) as [number, number, number, number];
+    const keypoints = newResult.body[i].keypoints // update keypoints
       .map((keypoint, j) => ({
         score: keypoint.score,
         part: keypoint.part,
@@ -522,24 +523,37 @@ function calcBuffered(newResult, localOptions) {
           y: bufferedResult.body[i].keypoints[j] ? ((localOptions.bufferedFactor - 1) * bufferedResult.body[i].keypoints[j].position.y + keypoint.position.y) / localOptions.bufferedFactor : keypoint.position.y,
         },
       }));
+    bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints }; // shallow clone plus updated values
   }
 
   // interpolate hand results
-  if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand));
-  for (let i = 0; i < newResult.hand.length; i++) { // update body: box, boxRaw, landmarks, annotations
-    bufferedResult.hand[i].box = newResult.hand[i].box
-      .map((box, j) => ((localOptions.bufferedFactor - 1) * bufferedResult.hand[i].box[j] + box) / localOptions.bufferedFactor);
-    bufferedResult.hand[i].boxRaw = newResult.hand[i].boxRaw
-      .map((box, j) => ((localOptions.bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + box) / localOptions.bufferedFactor);
-    bufferedResult.hand[i].landmarks = newResult.hand[i].landmarks
+  if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand)); // deep clone once
+  for (let i = 0; i < newResult.hand.length; i++) {
+    const box = newResult.hand[i].box // update box
+      .map((b, j) => ((localOptions.bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / localOptions.bufferedFactor);
+    const boxRaw = newResult.hand[i].boxRaw // update boxRaw
+      .map((b, j) => ((localOptions.bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / localOptions.bufferedFactor);
+    const landmarks = newResult.hand[i].landmarks // update landmarks
       .map((landmark, j) => landmark
         .map((coord, k) => ((localOptions.bufferedFactor - 1) * bufferedResult.hand[i].landmarks[j][k] + coord) / localOptions.bufferedFactor));
-    const keys = Object.keys(newResult.hand[i].annotations);
+    const keys = Object.keys(newResult.hand[i].annotations); // update annotations
+    const annotations = [];
     for (const key of keys) {
-      bufferedResult.hand[i].annotations[key] = newResult.hand[i].annotations[key]
+      annotations[key] = newResult.hand[i].annotations[key]
         .map((val, j) => val
           .map((coord, k) => ((localOptions.bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / localOptions.bufferedFactor));
     }
+    bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, landmarks, annotations }; // shallow clone plus updated values
+  }
+
+  // interpolate face results
+  if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) bufferedResult.face = JSON.parse(JSON.stringify(newResult.face)); // deep clone once
+  for (let i = 0; i < newResult.face.length; i++) {
+    const box = newResult.face[i].box // update box
+      .map((b, j) => ((localOptions.bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / localOptions.bufferedFactor);
+    const boxRaw = newResult.face[i].boxRaw // update boxRaw
+      .map((b, j) => ((localOptions.bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / localOptions.bufferedFactor);
+    bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw }; // shallow clone plus updated values
   }
 
   // interpolate person results
@@ -569,7 +583,7 @@ export async function all(inCanvas: HTMLCanvasElement, result: Result, drawOptio
   if (!(inCanvas instanceof HTMLCanvasElement)) return;
   if (localOptions.bufferedOutput) calcBuffered(result, localOptions); // do results interpolation
   else bufferedResult = result; // just use results as-is
-  face(inCanvas, result.face, localOptions); // face does have buffering
+  face(inCanvas, bufferedResult.face, localOptions); // face does have buffering
   body(inCanvas, bufferedResult.body, localOptions); // use interpolated results if available
   hand(inCanvas, bufferedResult.hand, localOptions); // use interpolated results if available
   // person(inCanvas, bufferedResult.persons, localOptions); // use interpolated results if available
diff --git a/src/object/centernet.ts b/src/object/centernet.ts
index cd5b03a0..bcd6bd89 100644
--- a/src/object/centernet.ts
+++ b/src/object/centernet.ts
@@ -24,18 +24,18 @@ export async function load(config) {
 }
 
 async function process(res, inputSize, outputShape, config) {
+  if (!res) return [];
   const results: Array<Item> = [];
   const detections = res.arraySync();
   const squeezeT = tf.squeeze(res);
   res.dispose();
   const arr = tf.split(squeezeT, 6, 1); // x1, y1, x2, y2, score, class
   squeezeT.dispose();
-  const stackT = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // tf.nms expects y, x
+  const stackT = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x
   const boxesT = stackT.squeeze();
   const scoresT = arr[4].squeeze();
   const classesT = arr[5].squeeze();
   arr.forEach((t) => t.dispose());
-  // @ts-ignore boxesT type is not correctly inferred
   const nmsT = await tf.image.nonMaxSuppressionAsync(boxesT, scoresT, config.object.maxDetected, config.object.iouThreshold, config.object.minConfidence);
   boxesT.dispose();
   scoresT.dispose();
@@ -44,7 +44,7 @@ async function process(res, inputSize, outputShape, config) {
   nmsT.dispose();
   let i = 0;
   for (const id of nms) {
-    const score = detections[0][id][4];
+    const score = Math.trunc(100 * detections[0][id][4]) / 100;
     const classVal = detections[0][id][5];
     const label = labels[classVal].label;
     const boxRaw = [
@@ -64,18 +64,16 @@ async function process(res, inputSize, outputShape, config) {
   return results;
 }
 
-export async function predict(image, config): Promise<Item[]> {
+export async function predict(input, config): Promise<Item[]> {
   if ((skipped < config.object.skipFrames) && config.skipFrame && (last.length > 0)) {
     skipped++;
     return last;
   }
   skipped = 0;
   return new Promise(async (resolve) => {
-    const outputSize = [image.shape[2], image.shape[1]];
-    const resize = tf.image.resizeBilinear(image, [model.inputSize, model.inputSize], false);
-
-    let objectT;
-    if (config.object.enabled) objectT = model.execute(resize, 'tower_0/detections');
+    const outputSize = [input.shape[2], input.shape[1]];
+    const resize = tf.image.resizeBilinear(input, [model.inputSize, model.inputSize]);
+    const objectT = config.object.enabled ? model.execute(resize, ['tower_0/detections']) : null;
     resize.dispose();
 
     const obj = await process(objectT, model.inputSize, outputSize, config);
diff --git a/wiki b/wiki
index 317a8fc7..78e6de45 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit 317a8fc76cd933cc38f59948ffade324fc8f1df2
+Subproject commit 78e6de4516ab49f47a906ec7778073b2dbbfed3f