EyeTheia: A Lightweight and Accessible Eye-Tracking Toolbox
Abstract
We introduce EyeTheia, a lightweight and open deep learning pipeline for webcam-based gaze estimation, designed for browser-based experimental platforms and real-world cognitive and clinical research. EyeTheia enables real-time gaze tracking using only a standard laptop webcam, combining MediaPipe-based landmark extraction with a convolutional neural network inspired by iTracker and optional user-specific fine-tuning. We investigate two complementary strategies: adapting a model pretrained on mobile data and training the same architecture from scratch on a desktop-oriented dataset. Validation results on MPIIFaceGaze show comparable performance between both approaches prior to calibration, while lightweight user-specific fine-tuning consistently reduces gaze prediction error. We further evaluate EyeTheia in a realistic Dot-Probe task and compare it to the commercial webcam-based tracker SeeSo SDK. Results indicate strong agreement in left-right gaze allocation during stimulus presentation, despite higher temporal variability. Overall, EyeTheia provides a transparent and extensible solution for low-cost gaze tracking, suitable for scalable and reproducible experimental and clinical studies. The code, trained models, and experimental materials are publicly available.
Keywords
Cite
@article{arxiv.2601.06279,
title = {EyeTheia: A Lightweight and Accessible Eye-Tracking Toolbox},
author = {Stevenson Pather and Niels Martignène and Arnaud Bugnet and Fouad Boutaleb and Fabien D'Hondt and Deise Santana Maia},
journal= {arXiv preprint arXiv:2601.06279},
year = {2026}
}
Comments
Code for the EyeTheia gaze-tracking model: https://github.com/patherstevenson/EyeTheia. Experimental platform for the cognitive neuroscience task: https://git.interactions-team.fr/INTERACTIONS/calypso/src/branch/main/src/lonely_tester