Ocular Authentication: Fusion of Gaze and Periocular Modalities
This work addresses secure authentication for users in VR or similar settings, presenting an incremental improvement by combining existing modalities.
The paper tackled user authentication by fusing gaze and periocular modalities in a calibration-free system, achieving results that consistently outperformed unimodal systems and surpassed the FIDO benchmark on a dataset of 9202 subjects.
This paper investigates the feasibility of fusing two eye-centric authentication modalities-eye movements and periocular images-within a calibration-free authentication system. While each modality has independently shown promise for user authentication, their combination within a unified gaze-estimation pipeline has not been thoroughly explored at scale. In this report, we propose a multimodal authentication system and evaluate it using a large-scale in-house dataset comprising 9202 subjects with an eye tracking (ET) signal quality equivalent to a consumer-facing virtual reality (VR) device. Our results show that the multimodal approach consistently outperforms both unimodal systems across all scenarios, surpassing the FIDO benchmark. The integration of a state-of-the-art machine learning architecture contributed significantly to the overall authentication performance at scale, driven by the model's ability to capture authentication representations and the complementary discriminative characteristics of the fused modalities.