Enhancing Eye Movement Biometrics for User Authentication via Continuous Gaze Offset Score Fusion
For researchers in eye movement biometrics, this paper provides an incremental improvement by showing that a previously overlooked feature (continuous gaze offset) can enhance authentication accuracy.
This work investigates whether continuous gaze offset can improve eye movement biometrics for user authentication. Results show that nonlinear fusion of gaze offset with existing features improves performance on two datasets, especially when fusing across multiple tasks.
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.