WebEyeTrack

Created by Eduardo Davalos, Yike Zhang, Namrata Srivastava, Yashvitha Thatigolta, Jorge A. Salas, Sara McFadden, Cho Sun-Joo, Amanda Goodwin, Ashwin TS, and Guatam Biswas from Vanderbilt University, Trinity University, and St. Mary's University

Project | Paper | Demo

WebEyeTrack is a framework that uses a lightweight CNN-based neural network to predict the (x,y) gaze point on the screen. The framework provides both a Python and JavaScript/TypeScript (client-side) versions to support research/testing and deployment via TS/JS. It performs few-shot gaze estimation by collecting samples on-device to adapt the model to account for unseen persons.

Getting Started

Deciding which version of WebEyeTrack depends on your purpose and target platform. Here is a table to help you determine which version to use:

Feature	Python Version	JavaScript Version
Purpose	Training, Research, and Testing	Deployment and Production
Primary Use Case	Model development and experimentation	Real-time inference in the browser
Supported Devices	CPU & GPU (desktop/server)	CPU (Web browser, mobile)
Model Access	Full access to model internals	Optimized for on-device inference and training
Extensibility	Highly customizable (e.g., few-shot learning, adaptation)	Minimal, focused on performance
Frameworks	TensorFlow / Keras	TensorFlow.js
Data Handling	Direct access to datasets and logs	Webcam stream, UI input

Go to the README (links below) to the corresponding Python/JS version to get stared using these packages.

• Python webeyetrack PYPI package
• JavaScript webeyetrack NPM package

Acknowledgements

The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305A150199 and R305A210347 to Vanderbilt University. The opinions expressed are those of the authors and do not represent views of the Institute or the U.S. Department of Education.

Reference

If you use this work in your research, please cite us using the following:

@misc{davalos2025webeyetrack,
	title={WEBEYETRACK: Scalable Eye-Tracking for the Browser via On-Device Few-Shot Personalization},
	author={Eduardo Davalos and Yike Zhang and Namrata Srivastava and Yashvitha Thatigotla and Jorge A. Salas and Sara McFadden and Sun-Joo Cho and Amanda Goodwin and Ashwin TS and Gautam Biswas},
	year={2025},
	eprint={2508.19544},
	archivePrefix={arXiv},
	primaryClass={cs.CV},
	url={https://arxiv.org/abs/2508.19544}
}

License

WebEyeTrack is open-sourced under the MIT License, which permits personal, academic, and commercial use with proper attribution. Feel free to use, modify, and distribute the project.