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.

本文提出了一种使用眼动模式进行生物识别的新框架，利用 fixations 和 saccades 提取特征，并使用 Gaussian Radial Basis Function Network 用于生物认证。使用文本阅读和屏幕上的随机点跟随等两种视觉刺激进行了测试，结果表明眼动模式是一种强大的生物证明模式，该算法已经在 BioEye 2015 数据库上进行了验证，并发现胜过其他所有方法。结合虹膜识别技术使用眼动动力学可能会导致一个牢固的防伪人员身份识别系统。