Polarization-resolved imaging improves eye tracking
Mantas Žurauskas, Tom Bu, Sanaz Alali, Beyza Kalkanli, Derek Shi, Fernando Alamos, Gauresh Pandit, Christopher Mei, Ali Behrooz, Ramin Mirjalili, Dave Stronks, Alexander Fix, Dmitri Model
偏振解决的近红外成像通过测量眼部组织反射的光的偏振状态,除了其强度之外,还为眼动跟踪增加了一种有用的光学对比度机制。 在本文中,我们演示了如何使用这种对比度来实现眼动追踪。 具体来说,我们证明由偏振过滤-阵列相机组成的偏振眼动(PET)系统与线性偏振近红外照明器相结合,可以揭示角膜上的颅骨和凝视信息模式的可跟踪特征,在强度仅图像中基本上不存在。 在346名参与者中,基于PET数据的卷积神经网络机器学习模型在名义条件下和眼睑闭塞,眼睛缓解变化和瞳孔变化的情况下,相对于容量匹配的强度基线,第95百分位绝对凝视误差减少了10-16%。 这些结果将光组织偏振效应与人机交互的实际收益联系起来,并将PET定位为未来可穿戴设备的一种简单,强大的传感模式。
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 fea...