Collarette Area Localization and Asymmetrical Support Vector Machines for Efficient Iris Recognition

Abstract

This paper presents an efficient iris recognition technique based on the zigzag collarette area localization and asymmetrical support vector machine. The deterministic feature sequence extracted from the iris images using the ID log-Gabor filters is applied to train the support vector machine (SVM). We use the multi- objective genetic algorithm (MOGA) to optimize the features and also to increase the overall recognition accuracy. The traditional SVM is modified to an asymmetrical SVM to treat the cases of the False Accept and the False Reject differently and also to handle the unbalanced data of a specific class with respect to the other classes. The proposed technique is computationally effective with a recognition rate of 97.70% on the ICE (Iris Challenge Evaluation) iris dataset.