Establishing a Baseline for Gaze-driven Authentication Performance in VR: A Breadth-First Investigation on a Very Large Dataset
arxiv(2024)
摘要
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
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.
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