Using Gaze for Behavioural Biometrics

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 3; p. 1262
• Main Authors: D’Amelio, Alessandro, Patania, Sabrina, Bursic, Sathya, Cuculo, Vittorio, Boccignone, Giuseppe
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 22.01.2023; MDPI
• Subjects: Analysis; Bayes Theorem; behaviour characteristics; Biometric identification; Biometric Identification - methods; biometric recognition; Biometrics; Biometry; Deep learning; Eye Movements; Eye-Tracking Technology; foraging theory; gaze identification; Neural networks; Virtual reality; visual attention; United States; Germany; visual attention; behaviour characteristics; foraging theory; gaze identification; biometric recognition; eye movements; Bayesian inference; machine learning; stochastic processes
• DOI: 10.3390/s23031262
• ISSN: 1424-8220

A principled approach to the analysis of eye movements for behavioural biometrics is laid down. The approach grounds in foraging theory, which provides a sound basis to capture the uniqueness of individual eye movement behaviour. We propose a composite Ornstein-Uhlenbeck process for quantifying the exploration/exploitation signature characterising the foraging eye behaviour. The relevant parameters of the composite model, inferred from eye-tracking data via Bayesian analysis, are shown to yield a suitable feature set for biometric identification; the latter is eventually accomplished via a classical classification technique. A proof of concept of the method is provided by measuring its identification performance on a publicly available dataset. Data and code for reproducing the analyses are made available. Overall, we argue that the approach offers a fresh view on either the analyses of eye-tracking data and prospective applications in this field.