Abstract
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.
Abstract (translated)
本文对 gaze-驱动身份验证的基准点进行了建立，以使用一个由 9202 人进行眼跟踪（ET）信号质量相当于现代消费级虚拟现实（VR）平台的大型数据集来回答基本研究问题。使用的数据集的大小至少是之前相关工作的数据集大小的十倍以上。为了使我们的模型在假拒绝率（FRR）为 3% 时，假接受率（FAR）为 1/50,000 时实现，需要估计双眼的角膜和视觉轴以及最小验证和注册持续时间。对于我们的模型，在画廊大小为 148,000 或更多时，识别准确度会降低到与机会水平准确性相当。我们得出的主要结论是，当由最先进的机器学习架构驱动时， gaze 身份验证可以达到 FIDO 标准所要求的精度。此外，我们还发现了一个与画廊大小相关的识别准确度下降趋势。
URL
https://arxiv.org/abs/2404.11798
