User Identification with LFI-Based Eye Movement Data Using Time and Frequency Domain Features

TL;DR

This work shows that high-frequency eye movement signals captured by laser-interferometry based eye-trackers encode biometric identity even without gaze data. By extracting a broad set of time- and frequency-domain features from velocity and distance signals and using a lightweight classifier (LightGBM), the authors achieve 93.14% accuracy and 2.52% EER at 1000 Hz with 5-second windows, and demonstrate robustness through multi-window majority voting. They also reveal how performance scales with sampling rate and window size, with static tasks generally easier to identify than dynamic ones and with notable privacy risks in LFI-based biometrics. The results point to feasible biometric authentication opportunities on resource-constrained wearables while underscoring privacy concerns that warrant privacy-preserving safeguards and further research.

Abstract

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.