  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.
