# Polarization-resolved imaging improves eye tracking

arXiv: https://arxiv.org/abs/2511.04652 · HTML: https://mlsanity.com/paper/2511.04652/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 22 references that are in the map.
- First posted on arXiv 6 Nov 2025.

## Builds on (most cited first, 1 of 1)

- [Accurate Eye Tracking from Dense 3D Surface Reconstructions using Single-Shot Deflectometry](https://mlsanity.com/paper/2308.07298/) (2023)

## Built on by (most cited first, 2 of 2)

- [Polarization-Based Eye Tracking with Personalized Siamese Architectures](https://mlsanity.com/paper/2603.25889/) (2026)
- [GazePrior: Zero-Shot AR/VR Eye Tracking via Learned 3D Gaze Reconstruction](https://mlsanity.com/paper/2605.22359/) (2026)

## Abstract (arXiv)

Polarization-resolved near-infrared imaging adds a useful optical contrast mechanism to eye tracking by measuring the polarization state of light reflected by ocular tissues in addition to its intensity. In this paper we demonstrate how this contrast can be used to enable eye tracking. Specifically, we demonstrate that a polarization-enabled eye tracking (PET) system composed of a polarization--filter--array camera paired with a linearly polarized near-infrared illuminator can reveal trackable features across the sclera and gaze-informative patterns on the cornea, largely absent in intensity-only images. Across a cohort of 346 participants, convolutional neural network based machine learning models trained on data from PET reduced the median 95th-percentile absolute gaze error by 10--16% relative to capacity-matched intensity baselines under nominal conditions and in the presence of eyelid occlusions, eye-relief changes, and pupil-size variation. These results link light--tissue polarization effects to practical gains in human--computer interaction and position PET as a simple, robust sensing modality for future wearable devices.