Human Library

3D Face Detection & Rotation Tracking, Face Description & Recognition,
Body Pose Tracking, 3D Hand & Finger Tracking,
Iris Analysis, Age & Gender & Emotion Prediction,
Gesture Recognition

JavaScript module using TensorFlow/JS Machine Learning library

• Browser:
  Compatible with both desktop and mobile platforms
  Compatible with CPU, WebGL, WASM backends
  Compatible with WebWorker execution
• NodeJS:
  Compatible with both software tfjs-node and
  GPU accelerated backends tfjs-node-gpu using CUDA libraries

Check out Live Demo for processing of live WebCam video or static images

Demos

• Demo Application
• Face Extraction, Description, Identification and Matching
• Face Extraction and 3D Rendering

Project pages

• Code Repository
• NPM Package
• Issues Tracker
• API Specification
• Change Log

Wiki pages

• Home
• Demos
• Installation
• Usage & Functions
• Configuration Details
• Output Details
• Face Recognition & Face Description
• Gesture Recognition
• Common Issues

Additional notes

• Notes on Backends
• Development Server
• Build Process
• Performance Notes
• Performance Profiling
• Platform Support
• List of Models & Credits

See issues and discussions for list of known limitations and planned enhancements

Suggestions are welcome!

---

Options

As presented in the demo application...

Examples

Training image:

Using static images:

Live WebCam view:

Face Similarity Matching:

Face3D OpenGL Rendering:

468-Point Face Mesh Defails:

---

Quick Start

Simply load Human directly from a CDN in your HTML file:

<script src="https://cdn.jsdelivr.net/npm/@vladmandic/human/dist/human.js"></script>

or

<script src="https://unpkg.dev/@vladmandic/human/dist/human.js"></script>

For details, including how to use Browser ESM version or NodeJS version of Human, see Installation

Example

Example simple app that uses Human to process video input and
draw output on screen using internal draw helper functions

// create instance of human with simple configuration using default values
const config = { backend: 'webgl' };
const human = new Human(config);

function detectVideo() {
  // select input HTMLVideoElement and output HTMLCanvasElement from page
  const inputVideo = document.getElementById('video-id');
  const outputCanvas = document.getElementById('canvas-id');
  // 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 next frame
    requestAnimationFrame(detectVideo);
  });
}

detectVideo();

---

Default models

Default models in Human library are:

• Face Detection: MediaPipe BlazeFace-Back
• Face Mesh: MediaPipe FaceMesh
• Face Description: HSE FaceRes
• Face Iris Analysis: MediaPipe Iris
• Emotion Detection: Oarriaga Emotion
• Body Analysis: PoseNet

Note that alternative models are provided and can be enabled via configuration
For example, PoseNet model can be switched for BlazePose model depending on the use case

For more info, see Configuration Details and List of Models

---

Human library is written in TypeScript 4.3
Conforming to JavaScript ECMAScript version 2020 standard
Build target is JavaScript EMCAScript version 2018

For details see Wiki Pages
and API Specification