Abstract
Biometric authentication based on iris patterns is used for personal identification. Important attributes to identity applications include accuracy, speed and template size. Iris patterns are segmented by considering the maximum area of the connected components in the binary images. The iris region is decomposed into subregions. Hu moments are applied to the minimum variance subregions (MVS). The summation of the moment values in these subregions is given as input to Support Vector Machine (SVM) and feed forward Neural Network (NN). The prominent results of False Rejection Rate (FRR) 2.5% and False Acceptance Rate (FAR) 0.0% was obtained for SVM. With NN classifier, prominent results of FRR 4.2% and FAR 0.0% are obtained.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.