Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns
International Journal of Computer Vision
High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Personal Identification Based on Iris Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Iris Recognition Using Collarette Boundary Localization
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
An improved handwritten Chinese character recognition system using support vector machine
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
An efficient iris segmentation method for recognition
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Localized iris image quality using 2-d wavelets
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
A human identification technique using images of the iris andwavelet transform
IEEE Transactions on Signal Processing
Efficient iris recognition by characterizing key local variations
IEEE Transactions on Image Processing
Improving Features Subset Selection Using Genetic Algorithms for Iris Recognition
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
Optimal Features Subset Selection Using Genetic Algorithms for Iris Recognition
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Level Set Approaches and Adaptive Asymmetrical SVMs Applied for Nonideal Iris Recognition
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Nonideal Iris Recognition Using Level Set Approach and Coalitional Game Theory
ICVS '09 Proceedings of the 7th International Conference on Computer Vision Systems: Computer Vision Systems
Engineering Applications of Artificial Intelligence
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This paper presents an iris recognition technique based on the zigzag collarette region for segmentation and asymmetrical support vector machine to classify the iris pattern. The deterministic feature sequence extracted from the iris images using the 1D log-Gabor filters is applied to train the support vector machine (SVM). We use the multi-objective genetic algorithm (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.