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.

利用大规模目光记录数据集，本研究建立了基准，旨在回答关于目光驱动认证性能的基础研究问题。研究发现，当配备先进的机器学习架构和足够大的训练数据集时，目光认证可以达到 FIDO 标准所要求的准确性。