@capitalhitelhaz/webgazer NPM

WebGazer.js

TL;DR

npm install
grunt
# Bump version number in package.json
npm publish --access public

WebGazer.js is an eye tracking library that uses common webcams to infer the eye-gaze locations of web visitors on a page in real time. The eye tracking model it contains self-calibrates by watching web visitors interact with the web page and trains a mapping between the features of the eye and positions on the screen. WebGazer.js is written entirely in JavaScript and with only a few lines of code can be integrated in any website that wishes to better understand their visitors and transform their user experience. WebGazer.js runs entirely in the client browser, so no video data needs to be sent to a server. WebGazer.js can run only if the user consents in giving access to their webcam.

Features

Real time gaze prediction on most major browsers
No special hardware - WebGazer.js uses common webcams
Self-calibration from clicks and cursor movements
Easy to integrate with a few lines of JavaScript
Swappable components for eye detection
Multiple gaze prediction models
Useful video feedback to user

Build the repository

If you want to build the repository from source follow these instructions:

# Ensure NodeJS is downloaded: https://nodejs.org/en/download/
# Install grunt-cli if not installed (this may require you to use `sudo` or run the console as Administrator)
npm install -g grunt-cli
git clone https://github.com/brownhci/WebGazer.git
npm install
# Run grunt to build the webgazer.js and webgazer.min.js file in the build directory
grunt

To use the webgazer script in the head of an HTML file add the async tag to ensure the clmtrackr does not collapse to a slower version

Examples

Examples of how WebGazer.js works can be found here.

How to run the Example HTML files

Within the /www directory there are two example HTML files:

calibration.html: This example includes additional user feedback, such as a 9-point calibration sequence, accuracy measurements and an informative help module.
collision.html: This example contains a game where the user can move an orange ball with their eyes, which in turn collides with blue balls.

To run the example files as a server:

# Clone the repository and download NodeJS using the steps listed above
# Move into the www directory and download the additional dependencies
cd www
npm install
# Run the webpage index.html as a server
browser-sync start --server --files "*"

Browser Support

The following browsers support WebGazer.js:

Google Chrome
Microsoft Edge
Mozilla Firefox
Opera
Safari

Your browser needs to support the getUserMedia API as seen here.

Publications

@inproceedings{papoutsaki2016webgazer,
author     = {Alexandra Papoutsaki and Patsorn Sangkloy and James Laskey and Nediyana Daskalova and Jeff Huang and James Hays},
title      = {{WebGazer}: Scalable Webcam Eye Tracking Using User Interactions},
booktitle  = {Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI-16)},
pages      = {3839--3845},
year       = {2016},
organization={AAAI}
}

@inproceedings{papoutsaki2017searchgazer,
author     = {Alexandra Papoutsaki and James Laskey and Jeff Huang},
title      = {SearchGazer: Webcam Eye Tracking for Remote Studies of Web Search},
booktitle  = {Proceedings of the ACM SIGIR Conference on Human Information Interaction \& 	Retrieval (CHIIR)},
year       = {2017},
organization={ACM}
}

Who We Are

Alexandra Papoutsaki
James Laskey
Aaron Gokaslan
Yuze He
Jeff Huang

Other Collaborators

Ida De Smet - Software engineering student at the University of Auckland
Elizabeth Stevenson - Software engineering student at the University of Auckland
Jack Wong - Software engineering student at the University of Auckland

Acknowledgements

Webgazer is developed based on the research that is done by Brown University. The work of the calibration example file was developed in the context of a course project with the aim to improve the feedback of WebGazer. It was proposed by Dr. Gerald Weber and his team Dr. Clemens Zeidler and Kai-Cheung Leung.

This research is supported by NSF grants IIS-1464061, IIS-1552663, and the Brown University Salomon Award.