FaceNet.js

A TypeScript library for face detection and identification, optimized for in-browser execution with native GPU and CPU support.

Also has helpful React hooks and components.

Demo

FaceNet.js Demo

Code for the demo is in the example folder here.

To run the demo locally from this repo, see Development.

Features

• Browser-Optimized: Built specifically for high-performance in-browser execution
• Real-time face detection with BlazeFace model
• Face recognition using FaceNet embeddings
• Support for both images and video streams
• Hardware acceleration with WebGL/WebGPU for blazing-fast performance
• Privacy-first: All processing happens locally in the browser

Installation

npm install facenet-js

The core package installs its pinned MediaPipe Tasks Vision runtime. React is an optional peer used by the facenet-js/react entry point; apps that import React providers or hooks should provide React 19 or newer.

or with yarn:

yarn add facenet-js

Quick Start

Vanilla JavaScript/TypeScript

import { FaceDetector } from 'facenet-js';

// Create a face detector instance with GPU acceleration (or CPU fallback)
const detector = new FaceDetector({
  device: 'GPU', // 'GPU' for WebGL/WebGPU acceleration, 'CPU' for compatibility
  mode: 'IMAGE', // 'IMAGE' for photos, 'VIDEO' for real-time streams
  minDetectionConfidence: 0.5,
  embeddingModelPath: '/models/facenet.tflite' // Optional: Path to FaceNet model for creating face embeddings
});

await detector.initialize();

// Detect faces in an image
const imageElement = document.getElementById('myImage') as HTMLImageElement;
const detections = await detector.detectFromImage(imageElement);

// Get face embeddings for recognition
if (detections.length > 0) {
  const embedding = await detector.embed({
    source: imageElement,
    detection: detections[0]
  });
}

React

import { ImageFaceDetectorProvider, useFaceDetector, useFaceSimilarity } from 'facenet-js/react';

function App() {
  return (
    <ImageFaceDetectorProvider
      options={{
        device: 'GPU',
        minDetectionConfidence: 0.5,
        embeddingModelPath: '/models/facenet.tflite'
      }}
    >
      <FaceDetectionComponent />
    </ImageFaceDetectorProvider>
  );
}

function FaceDetectionComponent() {
  const { faceDetector, isLoading, error } = useFaceDetector();

  if (isLoading || !faceDetector) {
    return null;
  }
  
  // The face detector is automatically initialized by the provider
  // Use faceDetector.detectFromImage() or faceDetector.detectFromVideo()
}

API Reference

FaceDetector

The main class for face detection and recognition.

Constructor

new FaceDetector(options: FaceDetectionOptions)

Options:

• device: 'CPU' | 'GPU' - Select computation device
  • 'GPU': Leverages WebGL/WebGPU for maximum performance
  • 'CPU': Fallback option for broader compatibility
• mode: 'IMAGE' | 'VIDEO' - Detection mode
  • 'IMAGE': Optimized for static images
  • 'VIDEO': Optimized for real-time video streams
• minDetectionConfidence: number - Minimum confidence threshold (0-1)
• embeddingModelPath: string - Path to the FaceNet embedding model (required for face recognition)
• detectionModelPath?: string - Optional custom path to face detection model
• wasmPath?: string - Optional custom path to WASM files

Methods

initialize(): Promise<void>

Initializes and loads the face detection models into browser memory. Must be called before detection.

detectFromImage(imageElement: HTMLImageElement): Detection[]

Detects faces in a static image.

detectFromVideo(videoElement: HTMLVideoElement, timestamp: number): Detection[]

Detects faces in a video frame.

embed(request: EmbeddingRequest): ImageEmbedderResult | null

Generates face embeddings for recognition.

Current inference methods return synchronously because the MediaPipe Tasks Vision APIs do. Examples may still use await; this is valid JavaScript and keeps call sites easy to migrate if a future worker-backed backend returns Promises.

EmbeddingRequest:

{
  source: HTMLImageElement | HTMLVideoElement;
  detection: Detection;
  timestamp?: number; // Required for video
}

React API

Providers

ImageFaceDetectorProvider

A React context provider for image-based face detection.

<ImageFaceDetectorProvider options={options}>
  {children}
</ImageFaceDetectorProvider>

VideoFaceDetectorProvider

A React context provider for video-based face detection.

<VideoFaceDetectorProvider options={options}>
  {children}
</VideoFaceDetectorProvider>

Hooks

useFaceDetector()

Access the face detector instance and its state.

const { faceDetector, isLoading, error } = useFaceDetector();

Returns:

• faceDetector: The initialized FaceDetector instance, or null while loading or after initialization fails
• isLoading: Boolean indicating if models are loading
• error: Error object if initialization failed

useFaceSimilarity(a, b, threshold?)

Calculate similarity between two face embeddings.

const similarity = useFaceSimilarity(embedding1, embedding2, 0.5);

Returns:

• similarity: Cosine similarity score (-1 to 1)
• isMatch: Boolean indicating if faces match (similarity > threshold)
• message: Human-readable similarity message

useWebcam()

Manage webcam access for face detection.

const { videoRef, isActive, error, startWebcam, stopWebcam } = useWebcam();

Usage Examples

Face Detection in Images

import { FaceDetector } from 'facenet-js';

async function detectFaces() {
  const detector = new FaceDetector({
    device: 'GPU',
    mode: 'IMAGE',
    minDetectionConfidence: 0.7,
    embeddingModelPath: '/models/facenet.tflite'
  });

  await detector.initialize();

  const img = document.querySelector('#photo') as HTMLImageElement;
  const faces = await detector.detectFromImage(img);

  console.log(`Found ${faces.length} faces`);
  
  faces.forEach((face, index) => {
    console.log(`Face ${index + 1}:`, {
      confidence: face.score,
      boundingBox: face.boundingBox
    });
  });
}

Real-time Video Face Detection

import { FaceDetector } from 'facenet-js';

async function startVideoDetection() {
  const detector = new FaceDetector({
    device: 'GPU',
    mode: 'VIDEO',
    minDetectionConfidence: 0.5,
    embeddingModelPath: '/models/facenet.tflite'
  });

  await detector.initialize();

  const video = document.querySelector('#webcam') as HTMLVideoElement;
  
  // Detection loop
  async function detect() {
    const faces = await detector.detectFromVideo(video, performance.now());
    
    // Process detected faces
    faces.forEach(face => {
      // Draw bounding boxes, etc.
    });
    
    requestAnimationFrame(detect);
  }
  
  detect();
}

Face Recognition

import { FaceDetector } from 'facenet-js';

async function compareFaces(img1: HTMLImageElement, img2: HTMLImageElement) {
  const detector = new FaceDetector({
    device: 'GPU',
    mode: 'IMAGE',
    minDetectionConfidence: 0.5,
    embeddingModelPath: '/models/facenet.tflite'
  });

  await detector.initialize();

  // Detect faces in both images
  const faces1 = await detector.detectFromImage(img1);
  const faces2 = await detector.detectFromImage(img2);

  if (faces1.length === 0 || faces2.length === 0) {
    console.log('No faces detected');
    return;
  }

  // Get embeddings
  const embedding1 = await detector.embed({
    source: img1,
    detection: faces1[0]
  });

  const embedding2 = await detector.embed({
    source: img2,
    detection: faces2[0]
  });

  if (embedding1 && embedding2) {
    // Calculate cosine similarity
    const similarity = FaceDetector.cosineSimilarity(
      embedding1.embeddings[0], 
      embedding2.embeddings[0]
    );
    
    console.log(`Face similarity: ${similarity}`);
  }
}

React Face Comparison Example

import { ImageFaceDetectorProvider, VideoFaceDetectorProvider, useFaceDetector, useFaceSimilarity } from 'facenet-js/react';
import { useState } from 'react';

function FaceComparisonApp() {
  const [imageEmbedding, setImageEmbedding] = useState(null);
  const [videoEmbedding, setVideoEmbedding] = useState(null);
  const similarity = useFaceSimilarity(imageEmbedding, videoEmbedding, 0.5);

  return (
    <div style={{ display: 'grid', gridTemplateColumns: '1fr 1fr', gap: '20px' }}>
      <ImageFaceDetectorProvider
        options={{
          device: 'GPU',
          minDetectionConfidence: 0.5,
          embeddingModelPath: '/models/facenet.tflite'
        }}
      >
        <ImageUploader onEmbedding={setImageEmbedding} />
      </ImageFaceDetectorProvider>

      <VideoFaceDetectorProvider
        options={{
          device: 'GPU',
          minDetectionConfidence: 0.5,
          embeddingModelPath: '/models/facenet.tflite'
        }}
      >
        <WebcamDetector onEmbedding={setVideoEmbedding} />
      </VideoFaceDetectorProvider>

      {similarity && (
        <div style={{ gridColumn: 'span 2', textAlign: 'center' }}>
          <h3>{similarity.isMatch ? '✅ Match!' : '❌ No Match'}</h3>
          <p>{similarity.message}</p>
        </div>
      )}
    </div>
  );
}

Model Information

This library uses:

• Face Detection: MediaPipe's BlazeFace model
• Face Recognition: FaceNet model for generating 128-dimensional face embeddings

Note: The FaceNet model file needs to be hosted and accessible. Place it in your public directory as /models/facenet.tflite.

Browser Support

FaceNet.js is designed as a browser-first library with excellent cross-browser compatibility:

• Chrome/Edge 90+
• Firefox 89+
• Safari 15.4+

Performance Notes:

• GPU Mode: Requires WebGL or WebGPU support for hardware acceleration
• CPU Mode: Works on all modern browsers, even without GPU support
• All processing happens locally in the browser - no server calls required

Development

To try the framework locally with the example app:

nvm install
npm ci
npm --prefix examples ci
npm run build
npm --prefix examples run dev -- --host 127.0.0.1

Open the Vite URL printed by the final command. If you already have Node 24 or newer active, you can skip nvm install. The example app serves examples/public/facenet.tflite, which is already linked to the repo's local models/facenet.tflite, so no model copy step is needed.

Maintainer checks:

npm test
npm run lint
npm run release:verify

TODO / Roadmap

• Face Clustering: Add support for clustering similar faces together
  • DBSCAN or hierarchical clustering algorithms
  • Automatic grouping of face embeddings
  • Configurable similarity thresholds

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• MediaPipe
• FaceNet (Schroff et al.)