Efficient multimodal ocular biometric system for person authentication based on iris texture and corneal shape

Abstract

Ocular biometrics refers to the use of features of the eye for person recognition. For instance, the unique and stable texture of the iris has been recognised as a powerful ocular biometric characteristic. In this study, the authors propose to improve biometric authentication with a multimodal ocular biometric system based on the iris pattern and the three-dimensional shape of the cornea. They show how the cornea can be used as a biometric trait for person recognition and then, they propose an intra-ocular fusion with iris features to improve the overall performance of the system. Feature extraction was done by modelling the shape of the cornea with a Zernike polynomial expansion. Then the best linear combinations of Zernike coefficients were found with linear discriminant analysis and used as biometric identifier. The iris texture was analysed with a typical methodology using Gabor filtering and phase encoding. The fusion was performed at the matching score level using min, max, sum and weighted-sum rule. The experimental results on a new database constructed for this bi-modal study showed impressive performance of the proposed ocular biometric system with equal error rate decreasing to 0% with the weighted-sum rule.