2025/05/12 by Süleyman Özdel, Ozdel, Suleyman, Johannes Meyer +5
Computer Science · Engineering · Medicine · #FOS: Computer and information sciences #Gait Recognition and Analysis #Gaze Tracking and Assistive Technology #Human-Computer Interaction (cs.HC) #Retinal Imaging and Analysis
paper · pdf · doi:10.48550/arxiv.2505.07326
openalex publication_date 2025/05/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Laser interferometry (LFI)-based eye-tracking systems provide an alternative to traditional camera-based solutions, offering improved privacy by eliminating the risk of direct visual identification. However, the high-frequency signals captured by LFI-based trackers may still contain biometric information that enables user identification. This study investigates user identification from raw high-frequency LFI-based eye movement data by analyzing features extracted from both the time and frequency domains. Using velocity and distance measurements without requiring direct gaze data, we develop a multi-class classification model to accurately distinguish between individuals across various activities. Our results demonstrate that even without direct visual cues, eye movement patterns exhibit sufficient uniqueness for user identification, achieving 93.14% accuracy and a 2.52% EER with 5-second windows across both static and dynamic tasks. Additionally, we analyze the impact of sampling rate and window size on model performance, providing insights into the feasibility of LFI-based biometric recognition. Our findings demonstrate the novel potential of LFI-based eye-tracking for user identification, highlighting both its promise for secure authentication and emerging privacy risks. This work paves the way for further research into high-frequency eye movement data.