{"ID":2871603,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.10969","arxiv_id":"2509.10969","title":"Gaze Authentication: Factors Influencing Authentication Performance","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2509.10969","url_pdf":"https://arxiv.org/pdf/2509.10969v2","authors":"[\"Dillon Lohr\",\"Michael J Proulx\",\"Mehedi Hasan Raju\",\"Oleg V Komogortsev\"]","published":"2025-09-13T20:03:11Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}