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arXiv Computer Vision By Dillon Lohr, Michael J Proulx, Mehedi Hasan Raju, Oleg V Komogortsev

Gaze Authentication: Factors Influencing Authentication Performance

Read the original on arXiv Computer Vision →

This paper investigates how key factors affect the performance of gaze-based authentication systems. Using a large in-house dataset of 8,849 subjects recorded with Meta Quest Pro–equivalent hardware and a 72 Hz video oculography pipeline, the study evaluates the impact of eye‑tracking signal quality, calibration settings, and simple filtering on neural‑network‑based authentication. The authors present performance results and an analysis of these influences.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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