Your browser doesn't support javascript.
loading
Using Gaze for Behavioural Biometrics.
D'Amelio, Alessandro; Patania, Sabrina; Bursic, Sathya; Cuculo, Vittorio; Boccignone, Giuseppe.
  • D'Amelio A; PHuSe Lab, Department of Computer Science, University of Milano Statale, Via Celoria 18, 20133 Milan, Italy.
  • Patania S; PHuSe Lab, Department of Computer Science, University of Milano Statale, Via Celoria 18, 20133 Milan, Italy.
  • Bursic S; PHuSe Lab, Department of Computer Science, University of Milano Statale, Via Celoria 18, 20133 Milan, Italy.
  • Cuculo V; Department of Psychology, University of Milano-Bicocca, Piazza dell'Ateneo Nuovo 1, 20126 Milan, Italy.
  • Boccignone G; PHuSe Lab, Department of Computer Science, University of Milano Statale, Via Celoria 18, 20133 Milan, Italy.
Sensors (Basel) ; 23(3)2023 Jan 22.
Article en En | MEDLINE | ID: mdl-36772302
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.
Asunto(s)
Palabras clave

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Movimientos Oculares / Identificación Biométrica Tipo de estudio: Prognostic_studies Idioma: En Año: 2023 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Movimientos Oculares / Identificación Biométrica Tipo de estudio: Prognostic_studies Idioma: En Año: 2023 Tipo del documento: Article