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.
Fields of Study & Subjects
Social Robot Interaction and HRI
User Authentication and Security Systems
Publication details
- Journal / source
- IEEE Access
- Volume
- 14
- Pages
- 59514–59527
- Publication date
- 1 January 2026
- Language
- EN