Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
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
Iris Recognition Using Collarette Boundary Localization
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
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
On Techniques for Angle Compensation in Nonideal Iris Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Efficient iris recognition by characterizing key local variations
IEEE Transactions on Image Processing
Iris recognition based on zigzag collarette region and asymmetrical support vector machines
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
A review of information fusion techniques employed in iris recognition systems
International Journal of Advanced Intelligence Paradigms
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In this paper, we propose an iris recognition method based on genetic algorithms (GA) to select the optimal features subset. The iris data usually contains huge number of textural features and a comparatively small number of samples per subject, which make the accurate iris patterns classification challenging. Feature selection scheme is used to identify the most important and irrelevant features from extracted features set of relatively high dimension based on some selection criterions. The traditional feature selection schemes require sufficient number of samples per subject to select the most representa-tive features sequence; however, it is not always practical to accumulate a large number of samples due to some security issues. In this paper, we propose GA to improve the feature subset selection by combining valuable outcomes from multiple feature selection methods. The main objective of GA is to achieve a balance among the recognition rate, the false accept rate, the false reject rate and the selected features subset size. This paper also motivates and introduces the use of Gaussian Mixture Model for iris pattern classification. The proposed technique is computationally effective with the recognition rates of 97.81 % and 96.23% on the ICE (Iris Challenge Evaluation) and the WVU (West Virginia University) iris datasets respectively.