Using Gaze for Behavioural Biometrics.
Bayesian inference
behaviour characteristics
biometric recognition
eye movements
foraging theory
gaze identification
machine learning
stochastic processes
visual attention
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
22 Jan 2023
22 Jan 2023
Historique:
received:
22
11
2022
revised:
15
01
2023
accepted:
20
01
2023
entrez:
11
2
2023
pubmed:
12
2
2023
medline:
15
2
2023
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 36772302
pii: s23031262
doi: 10.3390/s23031262
pmc: PMC9920149
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
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