利用偏振成像提升眼动追踪精度，无需复杂设备。
Polarization-resolved imaging improves eye tracking
- 结合偏振滤光阵列与近红外照明，捕捉眼球组织反射光的偏振特性。
- 在346人测试中，偏振眼动系统误差比纯强度系统降低10%~16%。
- 适合可穿戴设备，对眨眼、瞳孔变化等干扰有强鲁棒性。
偏振分辨的近红外成像通过测量眼组织反射光的偏振状态，为眼动追踪提供了额外的光学对比机制。本文展示了一种偏振增强型眼动追踪（PET）系统，由偏振滤光阵列相机与线性偏振近红外光源组成，可在巩膜上揭示可追踪特征，并在角膜上识别出与注视方向相关的信息模式，这些在仅依赖强度的图像中几乎不可见。在包含346名受试者的队列中，基于卷积神经网络的机器学习模型在标准条件下及存在眼皮遮挡、眼距变化和瞳孔大小波动时，相比容量匹配的强度基线系统，将中位数95百分位绝对注视误差降低了10%~16%。该结果将光-组织偏振效应与人机交互的实际性能提升联系起来，确立了PET作为未来可穿戴设备中一种简单且鲁棒的传感模态。
原文摘要 · Abstract (English)
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.
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