This paper examines the key factors that influence the performance of state-of-the-art gaze-based authentication. Experiments were conducted on a large-scale, in-house dataset comprising 8,849 subjects collected with Meta Quest Pro equivalent hardware running a video oculography-driven gaze estimation pipeline at 72~Hz. State of the neural network architecture was employed to study the influence of the following factors on authentication performance: eye tracking signal quality, various aspects of eye tracking calibration, and simple filtering on estimated raw gaze. This report provides performance results and their analysis.
@article{arxiv.2509.10969,
title = {Gaze Authentication: Factors Influencing Authentication Performance},
author = {Dillon Lohr and Michael J Proulx and Mehedi Hasan Raju and Oleg V Komogortsev},
journal= {arXiv preprint arXiv:2509.10969},
year = {2026}
}