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Using Gaze for Behavioural Biometrics
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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9920149/ https://www.ncbi.nlm.nih.gov/pubmed/36772302 http://dx.doi.org/10.3390/s23031262 |
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author | D’Amelio, Alessandro Patania, Sabrina Bursic, Sathya Cuculo, Vittorio Boccignone, Giuseppe |
author_facet | D’Amelio, Alessandro Patania, Sabrina Bursic, Sathya Cuculo, Vittorio Boccignone, Giuseppe |
author_sort | D’Amelio, Alessandro |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9920149 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99201492023-02-12 Using Gaze for Behavioural Biometrics D’Amelio, Alessandro Patania, Sabrina Bursic, Sathya Cuculo, Vittorio Boccignone, Giuseppe Sensors (Basel) Article 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. MDPI 2023-01-22 /pmc/articles/PMC9920149/ /pubmed/36772302 http://dx.doi.org/10.3390/s23031262 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article D’Amelio, Alessandro Patania, Sabrina Bursic, Sathya Cuculo, Vittorio Boccignone, Giuseppe Using Gaze for Behavioural Biometrics |
title | Using Gaze for Behavioural Biometrics |
title_full | Using Gaze for Behavioural Biometrics |
title_fullStr | Using Gaze for Behavioural Biometrics |
title_full_unstemmed | Using Gaze for Behavioural Biometrics |
title_short | Using Gaze for Behavioural Biometrics |
title_sort | using gaze for behavioural biometrics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9920149/ https://www.ncbi.nlm.nih.gov/pubmed/36772302 http://dx.doi.org/10.3390/s23031262 |
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