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 unique- ness of individual eye movement behaviour. We propose a composite Ornstein-Uhlenbeck process for quantifying the exploration/exploitation signature characterising the foraging eye behaviour. The rel- evant 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.

Using Gaze for Behavioural Biometrics / A. D'Amelio, S. Patania, S. Bursic, V. Cuculo, G. Boccignone. - In: SENSORS. - ISSN 1424-8220. - 23:3(2023 Feb), pp. 1262.1-1262.28. [10.3390/s23031262]

Using Gaze for Behavioural Biometrics

A. D'Amelio
Primo
;
S. Patania
Secondo
;
S. Bursic;V. Cuculo;G. Boccignone
Ultimo
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 unique- ness of individual eye movement behaviour. We propose a composite Ornstein-Uhlenbeck process for quantifying the exploration/exploitation signature characterising the foraging eye behaviour. The rel- evant 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.
biometric recognition; behaviour characteristics; gaze identification; foraging theory; visual attention; eye movements; stochastic processes; Bayesian inference; machine learning
Settore INF/01 - Informatica
Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni
feb-2023
22-gen-2023
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/952132
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