Abstract
This paper examines the key factors that influence the performance of state-of-the-art gaze-based authentication. Experiments were conducted on a large-scale, in-house dataset comprising 8,849 subjects collected with Meta Quest Pro equivalent hardware running a video oculography-driven gaze estimation pipeline at 72Hz. The state-of-the-art neural network architecture was employed to study the influence of the following factors on authentication performance: eye tracking signal quality, various aspects of eye tracking calibration, and simple filtering on estimated raw gaze. We found that using the same calibration target depth for eye tracking calibration, fusing calibrated and non-calibrated gaze, and improving eye tracking signal quality all enhance authentication performance. We also found that a simple three-sample moving average filter slightly reduces authentication performance in general. While these findings hold true for the most part, some exceptions were noted.
Abstract (translated)
本文研究了影响当前最先进的基于注视的认证性能的关键因素。实验是在一个大规模内部数据集上进行的，该数据集包含8,849名受试者的数据，这些数据是使用与Meta Quest Pro等效硬件采集的，并运行每秒72帧的视频眼动仪驱动的注视估计管道。为了研究以下因素对认证性能的影响，采用了最先进的神经网络架构：眼球追踪信号质量、眼球追踪校准的各种方面以及对原始估计注视进行简单滤波。 我们发现使用相同深度的目标进行眼球追踪校准、融合校准和非校准注视以及提高眼球追踪信号质量都能增强认证性能。另外还发现一般情况下简单的三样本移动平均滤波器会稍微降低认证性能。尽管这些研究结果在大多数情况下是成立的，但也注意到一些例外情况。
URL
https://arxiv.org/abs/2509.10969
