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Related Experiment Video

Updated: Oct 18, 2025

Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking

Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking

Published on: September 26, 2019

Biometric Identification Based on Eye Movement Dynamic Features.

Katarzyna Harezlak¹, Michal Blasiak¹, Pawel Kasprowski¹

¹Department of Applied Informatics, Silesian University of Technology, 44-100 Gliwice, Poland.

Sensors (Basel, Switzerland)

|September 28, 2021

Summary

This summary is machine-generated.

Biometric identification using eye movement signals achieved high accuracy with new features like frequency domain and Lyapunov exponent. Decision tree and random forest classifiers performed best, reaching 100% efficiency in some tests.

Keywords:

biometrics classification eye movement nonlinear time series analysis

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Last Updated: Oct 18, 2025

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Area of Science:

Biometrics
Human-Computer Interaction
Signal Processing

Background:

Biometric identification systems are crucial for security.
Eye movement signals offer a unique biometric modality.
Novel feature extraction is needed to improve accuracy.

Purpose of the Study:

To investigate new signal features for eye movement-based biometrics.
To evaluate the effectiveness of different machine learning classifiers.
To assess performance under varying data partitioning strategies.

Main Methods:

Extracted frequency domain and largest Lyapunov exponent features from 100-ms eye movement segments.
Utilized velocities and accelerations from previous studies.
Applied kNN, decision tree, naïve Bayes, and random forest classifiers.
Experiment involved 24 participants observing points on a screen across two sessions.

Main Results:

Decision tree and random forest classifiers showed good performance.
Random forest achieved 100% classification efficiency in one scenario.
Performance significantly decreased when training and testing sets were from different sessions.

Conclusions:

New features enhance eye movement biometrics.
Random forest and decision tree are effective classifiers for this application.
Session variability poses a challenge for robust biometric identification.