Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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
Feature Selection Using Multi-Objective Genetic Algorithms for Handwritten Digit Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
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
Detecting eye blink states by tracking iris and eyelids
Pattern Recognition Letters
Image understanding for iris biometrics: A survey
Computer Vision and Image Understanding
Biometric authentication: a machine learning approach
Biometric authentication: a machine learning approach
An efficient iris segmentation method for recognition
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Iris recognition with support vector machines
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
Improving iris recognition accuracy via cascaded classifiers
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiobjective GAs, quantitative indices, and pattern classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
New Methods in Iris Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The multicomponent AM-FM image representation
IEEE Transactions on Image Processing
Efficient iris recognition by characterizing key local variations
IEEE Transactions on Image Processing
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With the increasing demand for enhanced security, iris biometrics-based personal identification has become an interesting research topic in the field of pattern recognition. While most state-of-the-art iris recognition algorithms are focused on preprocessing iris images, important new directions have been identified recently in iris biometrics research. These include optimal feature selection and iris pattern classification. In this paper, we propose an iris recognition scheme based on Genetic Algorithms (GAs) and asymmetrical Support Vector Machines (SVMs). Instead of using the whole iris region, we elicit the iris information between the collarette and the pupillary boundaries to suppress effects of eyelids and eyelashes occlusions, and pupil dilation, and to minimize the matching error. To select the optimal feature subset together with increasing the overall recognition accuracy, we apply GAs with a new fitness function. The traditional SVMs are modified into asymmetrical SVMs to handle: (1) highly unbalanced sample proportion between two classes, and 2) different types of misclassification error that lead to different misclassification losses. Furthermore, the parameters of SVMs are optimized in order to improve the generalization performance. The proposed technique is computationally effective, with recognition rates of 97.80% and 95.70% on the Iris Challenge Evaluation (ICE) and the West Virginia University (WVU) iris datasets, respectively.