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

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 app for processing of live WebCam video or static images

• 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

Demos

• Main Application
• Face Extraction, Description, Identification and Matching
• Face Extraction and 3D Rendering
• Multithreaded Detection Showcasing Maximum Performance
• Documentation for Demo Applications

Project pages

• Code Repository
• NPM Package
• Issues Tracker
• TypeDoc API Specification: Human
• Change Log
• Current To-do List

Wiki pages

• Home
• Installation
• Usage & Functions
• Configuration Details
• Output Details
• Face Recognition & Face Description
• Gesture Recognition
• Common Issues
• Background and Benchmarks

Additional notes

• Notes on Backends
• Development Server
• Build Process
• Adding Custom Modules
• Performance Notes
• Performance Profiling
• Platform Support
• List of Models & Credits
• Security & Privacy Policy
• License & Usage Restrictions

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

Suggestions are welcome!

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Options

All options as presented in the demo application...

demo/index.html

Examples

Face Close-up:

Face under a high angle:

Full Person Details:

Pose Detection:

Body Segmentation and Background Replacement:

Large Group:

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

Face3D OpenGL Rendering:

demo/face3d

468-Point Face Mesh Defails:
(view in full resolution to see keypoints)

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Quick Start

Simply load Human (IIFE version) directly from a cloud CDN in your HTML file:
(pick one: jsdelirv, unpkg or cdnjs)

<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/2.1.4/human.js"></script>

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

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
  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

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 the next frame
    requestAnimationFrame(detectVideo);
  });
}

detectVideo();

or using async/await:

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

async function detectVideo() {
  const inputVideo = document.getElementById('video-id');
  const outputCanvas = document.getElementById('canvas-id');
  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 interpolated results for smooth video processing by separating detection and drawing loops:

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); // calculate next interpolated frame
    human.draw.all(outputCanvas, interpolated); // draw the frame
  }
  requestAnimationFrame(drawVideo); // run draw loop
}

detectVideo(); // start detection loop
drawVideo(); // start draw loop

And for even better results, you can run detection in a separate web worker thread

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Default models

Default models in Human library are:

• Face Detection: MediaPipe BlazeFace - Back variation
• Face Mesh: MediaPipe FaceMesh
• Face Iris Analysis: MediaPipe Iris
• Face Description: HSE FaceRes
• Emotion Detection: Oarriaga Emotion
• Body Analysis: MoveNet - Lightning variation
• Hand Analysis: MediaPipe Hands
• Body Segmentation: Google Selfie
• Object Detection: CenterNet
• Body Segmentation: Google Selfie

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 more info, see Configuration Details and List of Models

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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