face-api.js

JavaScript face recognition API for the browser and nodejs implemented on top of tensorflow.js core (tensorflow/tfjs-core)

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Tutorials

• face-api.js — JavaScript API for Face Recognition in the Browser with tensorflow.js
• Realtime JavaScript Face Tracking and Face Recognition using face-api.js’ MTCNN Face Detector
• Realtime Webcam Face Detection And Emotion Recognition - Video
• Easy Face Recognition Tutorial With JavaScript - Video

Table of Contents

• Features
• Running the Examples
• face-api.js for the Browser
• face-api.js for Nodejs
• Usage
  • Loading the Models
  • High Level API
  • Displaying Detection Results
  • Face Detection Options
  • Utility Classes
  • Other Useful Utility
• Available Models
  • Face Detection
  • Face Landmark Detection
  • Face Recognition
  • Face Expression Recognition
  • Age Estimation and Gender Recognition
• API Documentation

Features

Face Recognition

Face Landmark Detection

Face Expression Recognition

Age Estimation & Gender Recognition

Running the Examples

Clone the repository:

git clone https://github.com/justadudewhohacks/face-api.js.git

Running the Browser Examples

cd face-api.js/examples/examples-browser
npm i
npm start

Browse to http://localhost:3000/.

Running the Nodejs Examples

cd face-api.js/examples/examples-nodejs
npm i

Now run one of the examples using ts-node:

ts-node faceDetection.ts

Or simply compile and run them with node:

tsc faceDetection.ts
node faceDetection.js

face-api.js for the Browser

Simply include the latest script from dist/face-api.js.

Or install it via npm:

npm i face-api.js

face-api.js for Nodejs

We can use the equivalent API in a nodejs environment by polyfilling some browser specifics, such as HTMLImageElement, HTMLCanvasElement and ImageData. The easiest way to do so is by installing the node-canvas package.

Alternatively you can simply construct your own tensors from image data and pass tensors as inputs to the API.

Furthermore you want to install @tensorflow/tfjs-node (not required, but highly recommended), which speeds things up drastically by compiling and binding to the native Tensorflow C++ library:

npm i face-api.js canvas @tensorflow/tfjs-node

Now we simply monkey patch the environment to use the polyfills:

// import nodejs bindings to native tensorflow,
// not required, but will speed up things drastically (python required)
import '@tensorflow/tfjs-node';

// implements nodejs wrappers for HTMLCanvasElement, HTMLImageElement, ImageData
import * as canvas from 'canvas';

import * as faceapi from 'face-api.js';

// patch nodejs environment, we need to provide an implementation of
// HTMLCanvasElement and HTMLImageElement, additionally an implementation
// of ImageData is required, in case you want to use the MTCNN
const { Canvas, Image, ImageData } = canvas
faceapi.env.monkeyPatch({ Canvas, Image, ImageData })