Gaze-Detection

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Gaze-detection is a JavaScript library for detecting eye movements using machine learning. It enables developers to create gaze-controlled web experiences.

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At a glance

Pricing

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Free tier

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API

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Skill level

Technical

About

What is gaze-detection?

Gaze-detection is a JavaScript library designed to detect eye movements within web applications. It leverages machine learning capabilities, specifically utilizing TensorFlow.js's face landmark detection model, to accurately track a user's gaze. This tool empowers developers to build innovative, gaze-controlled experiences, offering a new dimension of interactivity for web-based projects. Its availability on GitHub suggests an open-source or community-driven development model.

Best used for

Building interactive web applications that respond to user eye movements for enhanced control and accessibility.

Common actions

Integrate eye tracking

Build interactive web apps

Enhance user experience

Develop accessibility tools

Implement gaze control

deepfakecollaborationlow-code/no-codeopen-sourceautomated workflow"AI Agents"face swappinggithub copilotworkflows

Capabilities

Key features

Eye movement detection
Machine learning powered
Gaze-controlled experiences
TensorFlow.js integration
JavaScript library

Target Audience

Web DevelopersFrontend DevelopersUX DesignersResearchers

Integrations

Not yet documented

Pricing & Plans

unknown

Free

FAQs

Is there a cost associated with using the Gaze-detection JavaScript library?

The description indicates Gaze-detection is a JavaScript library available on GitHub, strongly suggesting it is open-source and free to use. However, specific licensing details should be checked on its GitHub repository for full clarity on commercial use or redistribution.

What are the typical performance implications of running gaze detection in a web browser?

As Gaze-detection uses TensorFlow.js for machine learning-powered face landmark detection, performance can vary based on the user's device, browser, and webcam quality. It's crucial to test on target devices and consider optimizations for lower-end hardware to ensure a smooth user experience.

What kind of hardware or browser requirements does Gaze-detection have?

Gaze-detection requires a functional webcam for eye tracking and a modern web browser that supports WebGL and WebRTC for TensorFlow.js operations. Performance will be better on devices with dedicated GPUs or more powerful CPUs. Mobile device support would depend on browser and hardware capabilities.

Can Gaze-detection be used to track multiple users simultaneously?

The library description focuses on tracking 'a user's gaze.' While TensorFlow.js can detect multiple faces, integrating multi-user gaze tracking with this specific library would likely require custom development and careful management of individual tracking instances and their performance impact.

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