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

arXiv: https://arxiv.org/abs/2605.06810 · HTML: https://mlsanity.com/paper/2605.06810/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 7 May 2026.

## Builds on (most cited first, 8 of 8)

- [GazeBase: A Large-Scale, Multi-Stimulus, Longitudinal Eye Movement Dataset](https://mlsanity.com/paper/2009.06171/) (2020)
- [Eye Know You: Metric Learning for End-to-end Biometric Authentication Using Eye Movements from a Longitudinal Dataset](https://mlsanity.com/paper/2104.10489/) (2021)
- [GazeBaseVR, a large-scale, longitudinal, binocular eye-tracking dataset collected in virtual reality](https://mlsanity.com/paper/2210.07533/) (2022)
- [Deep Distributional Sequence Embeddings Based on a Wasserstein Loss](https://mlsanity.com/paper/1912.01933/) (2019)
- [Establishing a Baseline for Gaze-driven Authentication Performance in VR: A Breadth-First Investigation on a Very Large Dataset](https://mlsanity.com/paper/2404.11798/) (2024)
- [Evaluating Eye Tracking Signal Quality with Real-time Gaze Interaction Simulation](https://mlsanity.com/paper/2411.03708/) (2024)
- [Ocular Authentication: Fusion of Gaze and Periocular Modalities](https://mlsanity.com/paper/2505.17343/) (2025)
- [Filtering Eye-Tracking Data From an EyeLink 1000: Comparing Heuristic, Savitzky-Golay, IIR and FIR Digital Filters](https://mlsanity.com/paper/2303.02134/) (2023)

## Abstract (arXiv)

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.