Establishing a Baseline for Gaze-driven Authentication Performance in VR: A Breadth-First Investigation on a Very Large Dataset
<|reference_start|>Establishing a Baseline for Gaze-driven Authentication Performance in VR: A Breadth-First Investigation on a Very Large Dataset: 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.<|reference_end|>
@article{lohr2024establishing,
    title={Establishing a Baseline for Gaze-driven Authentication Performance in
  VR: A Breadth-First Investigation on a Very Large Dataset},
    author={Dillon Lohr, Michael J. Proulx, Oleg Komogortsev},
    journal={arXiv preprint arXiv:2404.11798},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.11798},
    primaryClass={cs.CV cs.HC}
}