Human: 3D Face Detection, Body Pose, Hand & Finger Tracking, Iris Tracking and Age & Gender Prediction

Code Repository: https://github.com/vladmandic/human
Documentation: https://github.com/vladmandic/human#readme
Live Demo: https://vladmandic.github.io/human/demo/demo-esm.html

Suggestions are welcome!

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Credits

This is an amalgamation of multiple existing models:

• Face Detection: MediaPipe BlazeFace
• Facial Spacial Geometry: MediaPipe FaceMesh
• Eye Iris Details: MediaPipe Iris
• Hand Detection & Skeleton: MediaPipe HandPose
• Body Pose Detection: PoseNet
• Age & Gender Prediction: SSR-Net

---

Installation

There are several ways to use Human library:

Important
This version of Human includes TensorFlow/JS (TFJS) 2.6.0 library which can be accessed via human.tf
You should not manually load another instance of tfjs, but if you do, be aware of possible version conflicts

There are multiple ways to use Human library, pick one that suits you:

1. IIFE script

This is simplest way for usage within Browser Simply download dist/human.js, include it in your HTML file & it's ready to use.

<script src="dist/human.js"><script>

IIFE script auto-registers global namespace human within Window object
Script is distributed in minified form with attached sourcemap

2. ESM module

2.1 With Bundler

If you're using bundler (such as rollup, webpack, esbuild) to package your client application, you can import ESM version of Human library which supports full tree shaking

  import human from 'dist/human.esm.js';

2.2 Using Script Module

You could use same syntax within your main JS file if it's imported with <script type="module">

  <script src="./index.js" type="module">

and then in your index.js

  import human from 'dist/human.esm.js';

Script is distributed in minified form with attached sourcemap

3. NPM module

Simmilar to ESM module, but with full sources as it points to actual sources entry point build/src/index.js instead
Recommended for NodeJS projects

Install with:

  npm install @tensorflow/tfjs @vladmandic/Human 

And then use with:

  import * as tf from '@tensorflow/tfjs';
  import human from '@vladmandic/Human';

Weights

Pretrained model weights are includes in ./models
Default configuration uses relative paths to you entry script pointing to ../models
If your application resides in a different folder, modify modelPath property in configuration of each module

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Demo

Demos are included in /demo:

• demo-esm: Demo using ESM module
• demo-iife: Demo using IIFE module

Both demos are identical, they just illustrate different ways to load Human library

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Usage

Human library does not require special initialization. All configuration is done in a single JSON object and all model weights will be dynamically loaded upon their first usage(and only then, Human will not load weights that it doesn't need according to configuration).

There is only ONE method you need:

import * as tf from '@tensorflow/tfjs';
import human from '@vladmandic/human';

// 'image': can be of any type of an image object: HTMLImage, HTMLVideo, HTMLMedia, Canvas, Tensor4D
// 'options': optional parameter used to override any options present in default configuration
const results = await human.detect(image, options?)

Additionally, Human library exposes several classes:

human.defaults // default configuration object
human.models   // dynamically maintained object of any loaded models
human.tf       // instance of tfjs used by human

---

Configuration

Below is output of human.defaults object
Any property can be overriden by passing user object during human.detect()
Note that user object and default configuration are merged using deep-merge, so you do not need to redefine entire configuration

human.defaults = {
  face: {
    enabled: true,
    detector: {
      modelPath: '../models/human/blazeface/model.json',
      maxFaces: 10,
      skipFrames: 5,
      minConfidence: 0.8,
      iouThreshold: 0.3,
      scoreThreshold: 0.75,
    },
    mesh: {
      enabled: true,
      modelPath: '../models/human/facemesh/model.json',
    },
    iris: {
      enabled: true,
      modelPath: '../models/human/iris/model.json',
    },
    age: {
      enabled: true,
      modelPath: '../models/human/ssrnet-imdb-age/model.json',
      skipFrames: 5,
    },
    gender: {
      enabled: true,
      modelPath: '../models/human/ssrnet-imdb-gender/model.json',
    },
  },
  body: {
    enabled: true,
    modelPath: '../models/human/posenet/model.json',
    maxDetections: 5,
    scoreThreshold: 0.75,
    nmsRadius: 20,
  },
  hand: {
    enabled: true,
    skipFrames: 5,
    minConfidence: 0.8,
    iouThreshold: 0.3,
    scoreThreshold: 0.75,
    detector: {
      anchors: '../models/human/handdetect/anchors.json',
      modelPath: '../models/human/handdetect/model.json',
    },
    skeleton: {
      modelPath: '../models/human/handskeleton/model.json',
    },
  },
};

Where:

• enabled: controls if specified modul is enabled (note: module is not loaded until it is required)
• modelPath: path to specific pre-trained model weights
• maxFaces, maxDetections: how many faces or people are we trying to analyze. limiting number in busy scenes will result in higher performance
• skipFrames: how many frames to skip before re-running bounding box detection (e.g., face position does not move fast within a video, so it's ok to use previously detected face position and just run face geometry analysis)
• minConfidence: threshold for discarding a prediction
• iouThreshold: threshold for deciding whether boxes overlap too much in non-maximum suppression
• scoreThreshold: threshold for deciding when to remove boxes based on score in non-maximum suppression
• nmsRadius: radius for deciding points are too close in non-maximum suppression

---

Outputs

Result of humand.detect() is a single object that includes data for all enabled modules and all detected objects:

result = {
  face: // <array of detected objects>
  [
    {
      confidence:  // <number>
      box:         // <array [x, y, width, height]>
      mesh:        // <array of 3D points [x, y, z]> (468 base points & 10 iris points)
      annotations: // <list of object { landmark: array of points }> (32 base annotated landmarks & 2 iris annotations)
      iris:        // <number> (relative distance of iris to camera, multiple by focal lenght to get actual distance)
      age:         // <number> (estimated age)
      gender:      // <string> (male or female)
    }
  ],
  body: // <array of detected objects>
  [
    {
      score:       // <number>,
      keypoints:   // <array of 2D landmarks [ score, landmark, position [x, y] ]> (17 annotated landmarks)
    }
  ],
  hand:            // <array of detected objects>
  [
    {
      confidence:    // <number>,
      box:           // <array [x, y, width, height]>,
      landmarks:     // <array of 3D points [x, y,z]> (21 points)
      annotations:   // <array of 3D landmarks [ landmark: <array of points> ]> (5 annotated landmakrs)
    }
  ]
}

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Build

If you want to modify the library and perform a full rebuild:
clone repository, install dependencies, check for errors and run full rebuild from which creates bundles from /src into /dist:

git clone https://github.com/vladmandic/human
cd human
npm install # installs all project dependencies
npm run lint
npm run build

Project is written in pure JavaScript, ECMAScript version 2020

Only project depdendency is @tensorflow/tfjs, the rest are devDependencies tools: eslint used for linting and esbuild used for bundling

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Performance

Performance will vary depending on your hardware, but also on number of resolution of input video/image, enabled modules as well as their parameters

For example, on a desktop with a low-end nVidia GTX1050 it can perform multiple face detections at 50+ FPS, but drops to 5-10 FPS on a medium complex images if all modules are enabled

Library can also be used on mobile devices

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Todo

• Improve detection of smaller faces
• Fix memory leak in face detector