New Matching Schemes for Iris Recognition

Biometric research has experienced significant advances in recent years given the need for more stringent security requirements. Iris recognition has been demonstrated to be an effcient and reliable technology for personal identification. In this book we employed three new matching schemes for iris recognition, the Scalar Product (SP), the Multi-dimensional Artificial Neural Networks (MDANN), and the Elastic Graph Matching (EGM). These three methods are trained and tested using two databases of gray scale eye images (CASIA and UBIRIS). They are trained using 996 and 723 iris images from the CASIA and UBIRIS database respectively. We have tested them using 915 and 448 iris images from the CASIA and UBIRIS database respectively. We have found that, there are 81 and 34 iris images from the CASIA and UBIRIS database respectively, are not used at all because of the failure analysis of locating iris for different causes. The Correct Recognition Rate (CCR) for the SP matching method is 98.26%, the CCR for the MDANN is 99.25%, and that for the EGM is 98.79%.