Using Gaze for Behavioural Biometrics

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.

Exploring Gaze Dynamics in Virtual Reality through Multiscale Entropy Analysis

“…This dataset captures the eye movements of participants engaged in a series of diverse and dynamic tasks, including vergence, smooth pursuit, video viewing, self-paced reading, and random saccades, collectively referred to as ‘VRG’, ‘PUR’, ‘VID’, ‘TEX’, and ‘RAN’. These tasks encompass a wide range of stimuli and activities typical in VR environments, making the dataset an invaluable resource for our study [ 28 , 29 ].…”