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.

D’Amelio, A., Patania, S., Bursic, S., Cuculo, V., Boccignone, G. (2023). Using Gaze for Behavioural Biometrics. SENSORS, 23(3), 1-28 [10.3390/s23031262].

Using Gaze for Behavioural Biometrics

Patania, S;
2023

Abstract

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.
Articolo in rivista - Articolo scientifico
Bayesian inference; behaviour characteristics; biometric recognition; eye movements; foraging theory; gaze identification; machine learning; stochastic processes; visual attention;
English
22-gen-2023
2023
23
3
1
28
1262
open
D’Amelio, A., Patania, S., Bursic, S., Cuculo, V., Boccignone, G. (2023). Using Gaze for Behavioural Biometrics. SENSORS, 23(3), 1-28 [10.3390/s23031262].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/553730
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