Establishing Robust Retinal Eye Tracking: A Weakly Supervised Algorithmic Framework

• URL: http://arxiv.org/abs/2605.09181v1
• Date: Sat, 09 May 2026 21:45:57 GMT
• Title: Establishing Robust Retinal Eye Tracking: A Weakly Supervised Algorithmic Framework
• Abstract summary: We propose a novel weakly-supervised, learning-based framework for robust retinal eye tracking.<n>Initial studies demonstrate high accuracy, achieving the 95th-percentile gaze error 0.45 deg across a cohort of 6 participants.
• Score: 35.596867355105786
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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