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 Simple Live Demo fully annotated app as a good start starting point (html)(code)

Check out Main Live Demo 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

Releases

Demos

Browser Demos

Full [Live] [Details]: Main browser demo app that showcases all Human capabilities
Simple [Live] [Details]: Simple demo in WebCam processing demo in TypeScript
Face Match [Live] [Details]: Extract faces from images, calculates face descriptors and simmilarities and matches them to known database
Face ID [Live] [Details]: Runs multiple checks to validate webcam input before performing face match to faces in IndexDB
Multi-thread [Live] [Details]: Runs each Human module in a separate web worker for highest possible performance
NextJS [Live] [Details]: Use Human with TypeScript, NextJS and ReactJS
3D Analysis [Live] [Details]: 3D tracking and visualization of heead, face, eye, body and hand
Avatar Bone Mapping [Live] [Details]: Human skeleton with full bone mapping using look and inverse kinematics controllers
Virtual Model Tracking [Live] [Details]: VR model with head, face, eye, body and hand tracking

NodeJS Demos

Main [Details]: Process images from files, folders or URLs using native methods
Canvas [Details]: Process image from file or URL and draw results to a new image file using node-canvas
Video [Details]: Processing of video input using ffmpeg
WebCam [Details]: Processing of webcam screenshots using fswebcam
Events [Details]: Showcases usage of Human eventing to get notifications on processing
Similarity [Details]: Compares two input images for similarity of detected faces
Face Match [Details]: Parallel processing of face match in multiple child worker threads
Multiple Workers [Details]: Runs multiple parallel human by dispaching them to pool of pre-created worker processes

Project pages

Wiki pages

Additional notes

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

Suggestions are welcome!

Examples

Visit Examples galery for more examples
https://vladmandic.github.io/human/samples/samples.html

Options

All options as presented in the demo application...

demo/index.html

Results Browser:
[ Demo -> Display -> Show Results ]

Advanced Examples

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

3D Rendering:

human-motion

Avatar Bone Mapping:

human-avatar

VR Model Tracking:

human-vrmmotion

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

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/2.1.5/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);
// 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);
  });
}

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
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:

// 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:

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

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: HandTrack & MediaPipe HandLandmarks
Body Segmentation: Google Selfie
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 more info, see Configuration Details and List of Models

Diagnostics

How to get diagnostic information or performance trace information

Human library is written in TypeScript 4.5
Conforming to latest JavaScript ECMAScript version 2021 standard
Build target is JavaScript EMCAScript version 2018

For details see Wiki Pages
and API Specification