Polarization-Based Eye Tracking with Personalized Siamese Architectures
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866912984253595648 |
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| author | Kalkanli, Beyza Bu, Tom Shakeri, Mahsa Fix, Alexander Stronks, Dave Model, Dmitri Žurauskas, Mantas |
| author_facet | Kalkanli, Beyza Bu, Tom Shakeri, Mahsa Fix, Alexander Stronks, Dave Model, Dmitri Žurauskas, Mantas |
| contents | Head-mounted devices integrated with eye tracking promise a solution for natural human-computer interaction. However, they typically require per-user calibration for optimal performance due to inter-person variability. A differential personalization approach using Siamese architectures learns relative gaze displacements and reconstructs absolute gaze from a small set of calibration frames. In this paper, we benchmark Siamese personalization on polarization-enabled eye tracking. For benchmarking, we use a 338-subject dataset captured with a polarization-sensitive camera and 850 nm illumination. We achieve performance comparable to linear calibration with 10-fold fewer samples. Using polarization inputs for Siamese personalization reduces gaze error by up to 12% compared to near-infrared (NIR)-based inputs. Combining Siamese personalization with linear calibration yields further improvements of up to 13% over a linearly calibrated baseline. These results establish Siamese personalization as a practical approach enabling accurate eye tracking. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_25889 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Polarization-Based Eye Tracking with Personalized Siamese Architectures Kalkanli, Beyza Bu, Tom Shakeri, Mahsa Fix, Alexander Stronks, Dave Model, Dmitri Žurauskas, Mantas Computer Vision and Pattern Recognition 68T07, 68T45 H.5.2; I.2.10; I.4.8; I.5.4 Head-mounted devices integrated with eye tracking promise a solution for natural human-computer interaction. However, they typically require per-user calibration for optimal performance due to inter-person variability. A differential personalization approach using Siamese architectures learns relative gaze displacements and reconstructs absolute gaze from a small set of calibration frames. In this paper, we benchmark Siamese personalization on polarization-enabled eye tracking. For benchmarking, we use a 338-subject dataset captured with a polarization-sensitive camera and 850 nm illumination. We achieve performance comparable to linear calibration with 10-fold fewer samples. Using polarization inputs for Siamese personalization reduces gaze error by up to 12% compared to near-infrared (NIR)-based inputs. Combining Siamese personalization with linear calibration yields further improvements of up to 13% over a linearly calibrated baseline. These results establish Siamese personalization as a practical approach enabling accurate eye tracking. |
| title | Polarization-Based Eye Tracking with Personalized Siamese Architectures |
| topic | Computer Vision and Pattern Recognition 68T07, 68T45 H.5.2; I.2.10; I.4.8; I.5.4 |
| url | https://arxiv.org/abs/2603.25889 |