Gaze Authentication: Factors Influencing Authentication Performance
This work addresses performance optimization for gaze-based authentication systems, but it is incremental as it focuses on refining existing methods rather than introducing new paradigms.
This paper investigated factors affecting gaze-based authentication performance, finding that using the same calibration target depth, fusing calibrated and non-calibrated gaze, and improving eye tracking signal quality enhance performance, while a simple moving average filter slightly reduces it, based on experiments with 8,849 subjects.
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 72Hz. The state-of-the-art 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. We found that using the same calibration target depth for eye tracking calibration, fusing calibrated and non-calibrated gaze, and improving eye tracking signal quality all enhance authentication performance. We also found that a simple three-sample moving average filter slightly reduces authentication performance in general. While these findings hold true for the most part, some exceptions were noted.