Analysis the Performance of Iris Recognition System by Using Hybrid Feature Extraction Methods and Matching By SVM Classifier

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Aparna G. Gale Dr. Suresh S. Salankar

Abstract

In today’s world, Iris recognition as physiological characteristics is one of the most reliable biometrics. It uses Iris of human eye plays an important role in accurate identification of individuals. Iris recognition system consists image acquisition, iris normalization, iris segmentation, features extraction and matching. Iris images are taken from CASIA iris VI database for study. In this paper we make a analysis the performance of iris recognition using combination of Haar transform, PCA and Block sum algorithm for iris verification to extract features on specific portion of the iris for improving the performance of an iris recognition system. The hybrid methods are evaluated by combining Haar transform and block sum algorithm. The classifier used in this paper is SVM classifier and decision taken by using FAR/ FRR and the experimental results show that this technique produces good performance on CASIA VI iris database.

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How to Cite
GALE, Aparna G.; SALANKAR, Dr. Suresh S.. Analysis the Performance of Iris Recognition System by Using Hybrid Feature Extraction Methods and Matching By SVM Classifier. ASIAN JOURNAL OF CONVERGENCE IN TECHNOLOGY, [S.l.], v. 3, n. 3, aug. 2017. ISSN 2350-1146. Available at: <http://asianssr.org/index.php/ajct/article/view/2824>. Date accessed: 18 oct. 2017.
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