Training Invariant Support Vector Machines
Machine Learning
High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
The steerable pyramid: a flexible architecture for multi-scale derivative computation
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
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
Multiobjective Evolutionary Algorithms and Applications (Advanced Information and Knowledge Processing)
Iris recognition based on score level fusion by using SVM
Pattern Recognition Letters
Optimal features subset selection and classification for iris recognition
Journal on Image and Video Processing - Regular
Engineering Applications of Artificial Intelligence
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Iris recognition is a prospering biometric method, but some technical difficulties still exist. This paper proposes an iris recognition method based on selected optimal features and statistical learning. To better represent the variation details in irises, we extract features from both spatial and frequency domain. Multi-objective genetic algorithm is then employed to optimize the features. Next step is doing classification of the optimal feature sequence. SVM has recently generated a great interest in the community of machine learning due to its excellent generalization performance in a wide variety of learning problems. We modified traditional SVM as non-symmetrical support vector machine to satisfy the different security requirements in iris recognition applications. Experimental data shows that the selected feature sequence represents the variation details of the iris patterns properly.