A score level fusion method for eye movement biometrics

A novel framework for eye movement biometrics is developed.Proposed algorithm achieved EER of 2.59% in a random stimulus task.Eye movement biometrics can be used to enhance counterfeit-resistance.The proposed method outperformed all the other methods in BioEye 2015 competition.The proposed method could be used with existing iris recognition systems since it provides inherent liveliness detection capabilities. Display Omitted 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.