Application of Multi-objective Genetic Algorithm and asymmetrical Support Vector Machine to improve the reliability of an iris recognition system

In this paper, we apply the multi-objective genetic algorithm (MOGA) and asymmetrical support vector machine to improve the performance of an iris recognition system. We utilize the collarette region instead of using the complete information of iris region for recognition purpose. The deterministic feature sequence extracted from the iris images using the 2-D Gabor wavelets is applied to train the support vector machine (SVM). We use the MOGA to optimize the features and also to increase the overall recognition accuracy based on the matching performance of the tuned SVM. 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 recognition rates of 97.70% and 95.60% on the ICE (Iris Challenge Evaluation) and the WVU (West Virginia University) iris datasets respectively.