[![](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 **AI-powered 3D Face Detection & Rotation Tracking, Face Description & Recognition,** **Body Pose Tracking, 3D Hand & Finger Tracking, Iris Analysis,** **Age & Gender & Emotion Prediction, Gaze Tracking, Gesture Recognition, Body Segmentation**
## Highlights - Compatible with most server-side and client-side environments and frameworks - Combines multiple machine learning models which can be switched on-demand depending on the use-case - Related models are executed in an attention pipeline to provide details when needed - Optimized input pre-processing that can enhance image quality of any type of inputs - Detection of frame changes to trigger only required models for improved performance - Intelligent temporal interpolation to provide smooth results regardless of processing performance - Simple unified API - Built-in Image, Video and WebCam handling [*Jump to Quick Start*](#quick-start)
## Compatibility - **Browser**: Compatible with both desktop and mobile platforms Compatible with *CPU*, *WebGL*, *WASM* backends Compatible with *WebWorker* execution Compatible with *WebView* - **NodeJS**: Compatibile with *WASM* backend for executions on architectures where *tensorflow* binaries are not available Compatible with *tfjs-node* using software execution via *tensorflow* shared libraries Compatible with *tfjs-node* using GPU-accelerated execution via *tensorflow* shared libraries and nVidia CUDA
## Releases - [Release Notes](https://github.com/vladmandic/human/releases) - [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
- [**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 - **Embedded** [[*Live*]](https://vladmandic.github.io/human/demo/video/index.html) [[*Details*]](https://github.com/vladmandic/human/tree/main/video/index.html): Even simpler demo with tiny code embedded in HTML file - **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 - **ElectronJS** [[*Details*]](https://github.com/vladmandic/human-electron): Use Human with TypeScript and ElectonJS to create standalone cross-platform apps - **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 - **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 *NodeJS demos may require extra dependencies which are used to decode inputs* *See header of each demo to see its dependencies as they are not automatically installed with `Human`* - **Main** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/nodejs): Process images from files, folders or URLs using native methods - **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` - **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 ## 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 - 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) ## Wiki pages - [**Home**](https://github.com/vladmandic/human/wiki) - [**Installation**](https://github.com/vladmandic/human/wiki/Install) - [**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) - [**Gesture Recognition**](https://github.com/vladmandic/human/wiki/Gesture) - [**Common Issues**](https://github.com/vladmandic/human/wiki/Issues) - [**Background and Benchmarks**](https://github.com/vladmandic/human/wiki/Background) ## Additional notes - [**Comparing Backends**](https://github.com/vladmandic/human/wiki/Backends) - [**Development Server**](https://github.com/vladmandic/human/wiki/Development-Server) - [**Build Process**](https://github.com/vladmandic/human/wiki/Build-Process) - [**Adding Custom Modules**](https://github.com/vladmandic/human/wiki/Module) - [**Performance Notes**](https://github.com/vladmandic/human/wiki/Performance) - [**Performance Profiling**](https://github.com/vladmandic/human/wiki/Profiling) - [**Platform Support**](https://github.com/vladmandic/human/wiki/Platforms) - [**Diagnostic and Performance trace information**](https://github.com/vladmandic/human/wiki/Diag) - [**Dockerize Human applications**](https://github.com/vladmandic/human/wiki/Docker) - [**List of Models & Credits**](https://github.com/vladmandic/human/wiki/Models) - [**Models Download Repository**](https://github.com/vladmandic/human-models) - [**Security & Privacy Policy**](https://github.com/vladmandic/human/blob/main/SECURITY.md) - [**License & Usage Restrictions**](https://github.com/vladmandic/human/blob/main/LICENSE)
*See [**issues**](https://github.com/vladmandic/human/issues?q=) and [**discussions**](https://github.com/vladmandic/human/discussions) for list of known limitations and planned enhancements* *Suggestions are welcome!*

## App Examples Visit [Examples gallery](https://vladmandic.github.io/human/samples/index.html) for more examples [](assets/samples.jpg)
## Options All options as presented in the demo application... [demo/index.html](demo/index.html) [](assets/screenshot-menu.png)
**Results Browser:** [ *Demo -> Display -> Show Results* ]
[](assets/screenshot-results.png)
## Advanced Examples 1. **Face Similarity Matching:** 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](demo/facematch/index.html) [](assets/screenshot-facematch.jpg) 2. **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) [](assets/screenshot-faceid.jpg)
3. **3D Rendering:** > [human-motion](https://github.com/vladmandic/human-motion) [](https://github.com/vladmandic/human-motion/raw/main/assets/screenshot-face.jpg) [](https://github.com/vladmandic/human-motion/raw/main/assets/screenshot-body.jpg) [](https://github.com/vladmandic/human-motion/raw/main/assets/screenshot-hand.jpg)
4. **VR Model Tracking:** > [human-three-vrm](https://github.com/vladmandic/human-three-vrm) > [human-bjs-vrm](https://github.com/vladmandic/human-bjs-vrm) [](https://github.com/vladmandic/human-three-vrm/raw/main/assets/human-vrm-screenshot.jpg) 5. **Human as OS native application:** > [human-electron](https://github.com/vladmandic/human-electron)
**468-Point Face Mesh Defails:** (view in full resolution to see keypoints) [](assets/facemesh.png)


## Quick Start Simply load `Human` (*IIFE version*) directly from a cloud CDN in your HTML file: (pick one: `jsdelirv`, `unpkg` or `cdnjs`) ```html ``` For details, including how to use `Browser ESM` version or `NodeJS` version of `Human`, see [**Installation**](https://github.com/vladmandic/human/wiki/Install)
## Code Examples 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); // select input HTMLVideoElement and output HTMLCanvasElement from page const inputVideo = document.getElementById('video-id'); const outputCanvas = document.getElementById('canvas-id'); function detectVideo() { // perform processing using default configuration human.detect(inputVideo).then((result) => { // result object will contain detected details // as well as the processed canvas itself // so lets first draw processed frame on canvas human.draw.canvas(result.canvas, outputCanvas); // then draw results on the same canvas human.draw.face(outputCanvas, result.face); human.draw.body(outputCanvas, result.body); human.draw.hand(outputCanvas, result.hand); human.draw.gesture(outputCanvas, result.gesture); // and loop immediate to the next frame requestAnimationFrame(detectVideo); return result; }); } detectVideo(); ``` or using `async/await`: ```js // create instance of human with simple configuration using default values const config = { backend: 'webgl' }; const human = new Human(config); // create instance of Human const inputVideo = document.getElementById('video-id'); const outputCanvas = document.getElementById('canvas-id'); async function detectVideo() { const result = await human.detect(inputVideo); // run detection human.draw.all(outputCanvas, result); // draw all results requestAnimationFrame(detectVideo); // run loop } detectVideo(); // start loop ``` or using `Events`: ```js // create instance of human with simple configuration using default values const config = { backend: 'webgl' }; const human = new Human(config); // create instance of Human const inputVideo = document.getElementById('video-id'); const outputCanvas = document.getElementById('canvas-id'); human.events.addEventListener('detect', () => { // event gets triggered when detect is complete human.draw.all(outputCanvas, human.result); // draw all results }); function detectVideo() { human.detect(inputVideo) // run detection .then(() => requestAnimationFrame(detectVideo)); // upon detect complete start processing of the next frame } detectVideo(); // start loop ``` or using interpolated results for smooth video processing by separating detection and drawing loops: ```js const human = new Human(); // create instance of Human const inputVideo = document.getElementById('video-id'); const outputCanvas = document.getElementById('canvas-id'); let result; async function detectVideo() { result = await human.detect(inputVideo); // run detection requestAnimationFrame(detectVideo); // run detect loop } async function drawVideo() { if (result) { // check if result is available 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 } 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


## 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 `