This paper performs the crucial work of establishing a baseline for gaze-driven authentication performance to begin answering fundamental research questions using a very large dataset of gaze recordings from 9202 people with a level of eye tracking (ET) signal quality equivalent to modern consumer-facing virtual reality (VR) platforms. The size of the employed dataset is at least an order-of-magnitude larger than any other dataset from previous related work. Binocular estimates of the optical and visual axes of the eyes and a minimum duration for enrollment and verification are required for our model to achieve a false rejection rate (FRR) of below 3% at a false acceptance rate (FAR) of 1 in 50,000. In terms of identification accuracy which decreases with gallery size, we estimate that our model would fall below chance-level accuracy for gallery sizes of 148,000 or more. Our major findings indicate that gaze authentication can be as accurate as required by the FIDO standard when driven by a state-of-the-art machine learning architecture and a sufficiently large training dataset.

翻译：本文通过使用来自9202名受试者的超大规模视线记录数据集，开展了为视线驱动认证性能建立基准的关键工作，以初步回答基础研究问题。该数据集中的眼动追踪信号质量与现代面向消费者的虚拟现实平台相当。所采用数据集的规模至少比先前相关工作中的任何其他数据集大一个数量级。我们的模型需要基于双眼的光轴与视轴估计，并设定注册和验证的最小时长，才能在五万分之一的错误接受率下实现低于3%的错误拒绝率。在识别准确率方面（随图库规模增大而下降），我们估计当图库规模达到148,000或更大时，模型的准确率将低于随机水平。我们的主要发现表明，当采用最先进的机器学习架构和足够大规模的训练数据集时，视线认证可以达到FIDO标准要求的精度水平。