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👀 Use machine learning in JavaScript to detect eye movements and build gaze-controlled experiences.
| Date | Stars |
|---|---|
| 2026-07-24 | 647 |
| 2026-07-25 | 647 |
| 2026-07-28 | 647 |
| 2026-07-30 | 647 |
| 2026-07-31 | 647 |
| 2026-08-06 | 647 |
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# Gaze-detection
Use machine learning in JavaScript to detect eye movements and build gaze-controlled experiences!
## Demo
Visit [https://gaze-keyboard.netlify.app/](https://gaze-keyboard.netlify.app/) _(Works well on mobile too!!)_ 😃
_Inspired by the Android application ["Look to speak"](https://play.google.com/store/apps/details?id=com.androidexperiments.looktospeak)._
Uses Tensorflow.js's [face landmark detection model](https://www.npmjs.com/package/@tensorflow-models/face-landmarks-detection).
## Detection
This tool detects when the user looks right, left, up and straight forward.
## How to use
### Install
As a module:
```bash
npm install gaze-detection --save
```
### Code sample
Start by importing it:
```js
import gaze from "gaze-detection";
```
Load the machine learning model:
```js
await gaze.loadModel();
```
Then, set up the camera feed needed for the detection. The `setUpCamera` method needs a `video` HTML element and, optionally, a camera device ID if you are using more than the default webcam.
```js
const videoElement = document.querySelector("video");
const init = async () => {
// Using the default webcam
await gaze.setUpCamera(videoElement);
// Or, using more camera input devices
const mediaDevices = await navigator.mediaDevices.enumerateDevices();
const camera = mediaDevices.find(
(device) =>
device.kind === "videoinput" &&
device.label.includes(/* The label from the list of available devices*/)
);
await gaze.setUpCamera(videoElement, camera.deviceId);
};
```
Run the predictions:
```js
const predict = async () => {
const gazePrediction = await gaze.getGazePrediction();
console.log("Gaze direction: ", gazePrediction); //will return 'RIGHT', 'LEFT', 'STRAIGHT' or 'TOP'
if (gazePrediction === "RIGHT") {
// do something when the user looks to the right
}
let raf = requestAnimationFrame(predict);
};
predict();
```
Stop the detection:
```js
cancelAnimationFrame(raf);
```
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