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Human Library

3D Face Detection & Rotation Tracking, Face Embedding & 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 CPU, WebGL, WASM backends
  Compatible with both desktop and mobile platforms
  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 Embedding
• Gesture Recognition

Additional notes

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

Default models

Default models in Human library are:

• Face Detection: MediaPipe BlazeFace-Back
• Face Mesh: MediaPipe FaceMesh
• Face Iris Analysis: MediaPipe Iris
• Emotion Detection: Oarriaga Emotion
• Gender Detection: Oarriaga Gender
• Age Detection: SSR-Net Age IMDB
• Body Analysis: PoseNet
• Face Embedding: BecauseofAI MobileFace Embedding

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

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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

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

Suggestions are welcome!

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