Ocular Authentication: Fusion of Gaze and Periocular Modalities

TL;DR

This study explores a novel multimodal eye-based authentication system combining gaze and periocular data, demonstrating superior performance over individual modalities in a large-scale VR-like dataset.

Contribution

It introduces a calibration-free multimodal authentication approach that fuses gaze and periocular modalities within a unified pipeline, evaluated on a large dataset.

Findings

Multimodal system outperforms unimodal counterparts across all scenarios.

The approach surpasses the FIDO benchmark.

State-of-the-art machine learning architecture enhances authentication accuracy.

Abstract

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.