A Score-level Fusion Method for Eye Movement Biometrics

TL;DR

This paper introduces a new score-level fusion method using eye movement patterns for biometric authentication, demonstrating improved accuracy and robustness over existing methods on the BioEye 2015 database.

Contribution

It presents a novel framework combining fixation and saccade features with a Gaussian RBF network and score fusion for enhanced biometric identification.

Findings

Outperforms existing biometric methods on BioEye 2015 database

Effective with different stimuli: dot following and text reading

Eye movement biometrics are resistant to spoofing

Abstract

This paper proposes a novel framework for the use of eye movement patterns for biometric applications. Eye movements contain abundant information about cognitive brain functions, neural pathways, etc. In the proposed method, eye movement data is classified into fixations and saccades. Features extracted from fixations and saccades are used by a Gaussian Radial Basis Function Network (GRBFN) based method for biometric authentication. A score fusion approach is adopted to classify the data in the output layer. In the evaluation stage, the algorithm has been tested using two types of stimuli: random dot following on a screen and text reading. The results indicate the strength of eye movement pattern as a biometric modality. The algorithm has been evaluated on BioEye 2015 database and found to outperform all the other methods. Eye movements are generated by a complex oculomotor plant which is very hard to spoof by mechanical replicas. Use of eye movement dynamics along with iris recognition technology may lead to a robust counterfeit-resistant person identification system.