Retinal image-based eye tracking is widely used in ophthalmic imaging and vision science, and is a promising path to deliver higher gaze accuracy than the pupil- and cornea-based approaches commonly used in modern AR/VR devices. Nevertheless, existing retinal tracking algorithms still primarily rely on classical template-matching registration, which can be insufficiently robust to retinal feature variability and real-world imaging conditions. In this work, we propose a novel weakly-supervised, learning-based framework for robust retinal eye tracking. Initial studies demonstrate high accuracy, achieving the 95th-percentile gaze error < 0.45 deg across a cohort of 6 participants.
@article{arxiv.2605.09181,
title = {Establishing Robust Retinal Eye Tracking: A Weakly Supervised Algorithmic Framework},
author = {Bo Wen and Dillon Lohr and Yatong An and Pushkar Anand and Alexander Fix and Ruobing Qian and Catherine A. Fromm and Yimin Ding and Truong Nguyen and Mohamed El-Haddad and Francesco La Rocca},
journal= {arXiv preprint arXiv:2605.09181},
year = {2026}
}
Comments
2026 IEEE International Conference on Image Processing (Accepted for Publication)