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

Content Provider	IEEE Xplore Digital Library
Author	Roy, K. Bhattacharya, P.
Copyright Year	2007
Description	Author affiliation: Concordia Univ., Montreal (Roy, K.; Bhattacharya, P.)
Abstract	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.
Starting Page	1952
Ending Page	1957
File Size	716821
Page Count	6
File Format	PDF
ISBN	9781424409907
DOI	10.1109/ICSMC.2007.4413633
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-07
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genetic algorithms Support vector machines Iris recognition Ice Eyelids Support vector machine classification Character recognition Humans Image segmentation NIST
Content Type	Text
Resource Type	Article

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