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：本文从事了建立凝视驱动身份验证性能基准的关键工作，利用包含9202人凝视记录的超大数据集（眼动追踪信号质量等同于现代消费级虚拟现实平台），开始回答基础性研究问题。所采用数据集规模至少比以往相关工作的任何数据集大一个数量级。我们的模型需要双眼光学轴和视觉轴的双目估计值，以及最短的注册与验证时长，才能在错误接受率为五万分之一时实现低于3%的错误拒绝率。在随库容量增大而下降的识别准确率方面，我们估计当库容量达到148,000或更多时，模型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的训练数据集时，凝视身份验证可以达到FIDO标准要求的准确度。