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 72~Hz. State of the 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. This report provides performance results and their analysis.

翻译：本文研究了影响当前最先进凝视身份验证（gaze-based authentication）性能的关键因素。实验基于一个大规模内部数据集进行，该数据集包含8,849名受试者，使用与Meta Quest Pro相当的硬件设备采集，运行基于视频眼动描记法的72赫兹凝视估计流程。采用最先进的神经网络架构，探讨了以下因素对身份验证性能的影响：眼动追踪信号质量、眼动追踪校准的各个方面，以及对估计原始凝视数据的简单滤波。本报告提供了性能结果及其分析。