Enhancing Eye Movement Biometrics for User Authentication via Continuous Gaze Offset Score Fusion

TL;DR

This paper investigates whether combining continuous gaze offset with existing eye movement biometrics enhances user authentication accuracy, especially in noisy tracking conditions, using fusion methods on multiple datasets.

Contribution

It demonstrates that fusion of gaze offset with traditional biometrics improves authentication performance, particularly with nonlinear fusion and multi-task data.

Findings

Fusion improves biometric performance on both datasets.

Nonlinear fusion yields better results than linear fusion.

Multi-task fusion further enhances authentication accuracy.

Abstract

Eye movement biometrics (EMB) use subject-specific gaze dynamics for user authentication and identification. Recent deep learning-based EMB systems achieve strong performance by modeling temporal eye movement behavior. However, these systems typically overlook continuous gaze offset, despite prior evidence that it contains user-discriminative information. This work examines whether continuous gaze offset can improve biometric performance when combined with existing biometric features. We evaluate linear and nonlinear fusion methods on two publicly available datasets, collected via the lab-grade eye tracker and virtual reality headset across multiple tasks and observation durations. Results indicate that fusion offers performance benefits on both datasets, particularly when using nonlinear fusion. Additionally, fusing biometric information across multiple tasks further improves authentication performance. These findings support the hypothesis that continuous gaze offset may serve as useful auxiliary information under conditions of degraded or noisy eye tracking.