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justadudewhohacks/face-api.js

★ 17,848TypeScriptAudience · developerComplexity · 2/5Setup · easy

TLDR

A JavaScript library that detects faces, reads expressions, estimates age and gender, and recognizes people using pretrained models that run entirely in a web browser or Node.js, no server required.

Mindmap

mindmap
  root((face-api.js))
    What it does
      Face detection
      Face recognition
      Expression detection
      Age and gender estimate
    Tech stack
      TypeScript
      TensorFlow.js
      Node.js
    Use cases
      Webcam demos
      Photo grouping
      Interactive apps
    Features
      Multiple detector models
      No server required
      Buffer protocol support
    Audience
      Web developers
      Node.js developers
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Things people build with this

USE CASE 1

Add real-time face detection to a webcam video stream and draw bounding boxes on a canvas overlay in the browser.

USE CASE 2

Build a photo gallery app that automatically groups pictures by recognized person using face descriptors.

USE CASE 3

Create a browser experiment that detects facial expressions and triggers animations in response to smiles.

Tech stack

TypeScriptJavaScriptTensorFlow.jsNode.js

Getting it running

Difficulty · easy Time to first run · 30min

Model weight files must be self-hosted and loaded before inference, Node.js use also requires the node-canvas package.

License terms not described in the explanation.

In plain English

face-api.js is a JavaScript library for doing face detection and face recognition right inside a web browser or a Node.js program. It is built on top of tensorflow.js, the JavaScript version of the TensorFlow machine-learning toolkit, so all the math runs in the browser or in Node using a native TensorFlow binding. There is no server call needed once the model files are loaded, which makes it handy for privacy-sensitive or offline use. The library bundles several pretrained neural networks and exposes them through a high-level API. You feed it an image, video, or canvas element, and it can find faces in the frame, mark 68 landmark points on each face, compute a numeric descriptor used to recognize who a face belongs to, classify the facial expression, and estimate age and gender. Internally it ships multiple detector options including an SSD MobileNet v1 model, a Tiny Face Detector, and a tiny YOLO v2, plus separate networks for landmarks, recognition, expressions, and age and gender. To use them you copy the model manifest and weight shard files into your project, then call loadFromUri or loadFromDisk to pull them into memory, and then call helpers like detectAllFaces on an input. You would reach for this when adding face-aware features to a web or Node app, a webcam demo that draws boxes around faces, a photo gallery that groups pictures by person, or an interactive toy that reacts to smiles. The repository is written in TypeScript and compiles to a browser bundle in dist as well as an npm package. Node usage is supported by polyfilling browser image and canvas APIs, the README suggests the node-canvas package and recommends @tensorflow/tfjs-node for speed.

Copy-paste prompts

Prompt 1
Using face-api.js, write the JavaScript to detect all faces in a live webcam stream and draw bounding boxes on a canvas overlay in real time.
Prompt 2
Show me how to load the face recognition model in face-api.js and compare two face descriptors to decide if they belong to the same person.
Prompt 3
Write a Node.js script using face-api.js and node-canvas to detect facial expressions in a local image file and print the results.
Prompt 4
How do I use face-api.js to estimate a person's age and gender from an image element on a web page?
Prompt 5
Set up face-api.js with @tensorflow/tfjs-node in a Node.js project for faster CPU inference.
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