الفهرس 
Chapter 1Only 14 pages are availabe for public view
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Abstract
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 efficient and reliable technology for personal identification. In this thesis 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%.