Establishing Robust Retinal Eye Tracking: A Weakly Supervised Algorithmic Framework
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917477589450752 |
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| author | Wen, Bo Lohr, Dillon An, Yatong Anand, Pushkar Fix, Alexander Qian, Ruobing Fromm, Catherine A. Ding, Yimin Nguyen, Truong El-Haddad, Mohamed La Rocca, Francesco |
| author_facet | Wen, Bo Lohr, Dillon An, Yatong Anand, Pushkar Fix, Alexander Qian, Ruobing Fromm, Catherine A. Ding, Yimin Nguyen, Truong El-Haddad, Mohamed La Rocca, Francesco |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_09181 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Establishing Robust Retinal Eye Tracking: A Weakly Supervised Algorithmic Framework Wen, Bo Lohr, Dillon An, Yatong Anand, Pushkar Fix, Alexander Qian, Ruobing Fromm, Catherine A. Ding, Yimin Nguyen, Truong El-Haddad, Mohamed La Rocca, Francesco Computer Vision and Pattern Recognition Emerging Technologies Image and Video Processing 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. |
| title | Establishing Robust Retinal Eye Tracking: A Weakly Supervised Algorithmic Framework |
| topic | Computer Vision and Pattern Recognition Emerging Technologies Image and Video Processing |
| url | https://arxiv.org/abs/2605.09181 |