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Related Experiment Video

Updated: Aug 10, 2025

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking

Published on: December 16, 2010

Using Gaze for Behavioural Biometrics.

Alessandro D'Amelio¹, Sabrina Patania¹, Sathya Bursic^1,2

¹PHuSe Lab, Department of Computer Science, University of Milano Statale, Via Celoria 18, 20133 Milan, Italy.

Sensors (Basel, Switzerland)

|February 11, 2023

Summary

This summary is machine-generated.

This study introduces a novel method using foraging theory and eye movement analysis for unique behavioral biometrics. The approach quantifies individual exploration patterns for reliable identification.

Keywords:

Bayesian inference behaviour characteristics biometric recognition eye movements foraging theory gaze identification machine learning stochastic processes visual attention

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Last Updated: Aug 10, 2025

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Published on: December 16, 2010

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing

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Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Published on: November 14, 2018

Area of Science:

Behavioral Biometrics
Computational Neuroscience
Data Science

Background:

Traditional biometric systems face challenges in uniqueness and spoofing.
Eye movement patterns offer a unique and intrinsic behavioral signature.
Foraging theory provides a framework for understanding exploration behaviors.

Purpose of the Study:

To develop a principled approach for analyzing eye movements for behavioral biometrics.
To leverage foraging theory to capture individual uniqueness in eye movement behavior.
To establish a robust method for biometric identification using eye-tracking data.

Main Methods:

A composite Ornstein-Uhlenbeck process was proposed to quantify the exploration/exploitation signature.
Bayesian analysis was employed to infer model parameters from eye-tracking data.
A classical classification technique was used for biometric identification based on inferred features.

Main Results:

The inferred parameters from the composite model proved effective as a feature set for biometric identification.
Proof of concept demonstrated identification performance on a publicly available dataset.
Data and code are available for reproducibility.

Conclusions:

The proposed approach offers a novel perspective on eye-tracking data analysis.
This method presents promising prospective applications in behavioral biometrics.
Foraging theory provides a strong foundation for unique biometric feature extraction.