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

TL;DR

This paper demonstrates that high-frequency LFI-based eye movement data can be used for accurate user identification, revealing biometric information without visual cues, thus offering both secure authentication potential and privacy concerns.

Contribution

It introduces a novel approach using time and frequency domain features from LFI-based eye data for user identification, achieving high accuracy without direct gaze information.

Findings

Achieved 93.14% accuracy in user identification

Demonstrated the impact of sampling rate and window size on performance

Showed biometric uniqueness in eye movement patterns from LFI data

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.