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International Conference on Advanced Computer Theory and Engineering (ICACTE 2009)
Xie Yi
Xie Yi
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ASME Press
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Biometric recognition suggests a reliable solution to the problem of user authentication in our networked society. Among all biometrics, palmprint-based recognition is one of the most reliable personal identification methods. In this study, a new approach to the palmprint recognition phase is presented. 2D Gabor filters are used for feature extraction of palmprints. After Gabor filtering, standard deviations are computed in order to generate the palmprint feature vector. In addition, Genetic algorithm based feature selection is used to select the best feature subset from the palmprint feature set. Four different algorithms of Artificial Neural Networks are then applied to the feature vectors for recognition of the people. Recognition rate equal to 98 percent are obtained by using conjugate gradient algorithms.

Key Words
1 Introduction
2. Palmprint Acquisition and Palmprint Features Using Gabor Filter Bank
3 Recognition of Palmprint Features Using Artificial Neural Network
4. Palmprint Feature Selection
5. Experimental Results
6. Conclusions
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