研究眼动认证性能的关键影响因素
Gaze Authentication: Factors Influencing Authentication Performance
- 用神经网络分析眼动信号质量、校准方式和滤波对认证的影响
- 在8849人数据集上测试，验证各因素对准确率的作用
- 适合安全认证与眼动交互系统设计者参考
本文研究了当前最先进的基于眼动的认证性能所受的关键因素影响。实验基于一个大规模自建数据集，包含8,849名受试者，使用与Meta Quest Pro相当的硬件，以72~120Hz采样率运行基于视频眼动追踪的注视估计流程。采用先进的神经网络架构，分析眼动信号质量、不同眼动校准方法以及原始注视估计值的简单滤波处理对认证性能的影响。报告提供了详细的性能结果及其分析。
原文摘要 · Abstract (English)
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.
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