Online Palmprint Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ordinal Palmprint Represention for Personal Identification
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A palmprint classification method based on finite ridgelet transformation and SVM
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
Palmprint based recognition system using phase-difference information
Future Generation Computer Systems
A New Automatic Parameter Setting Method of a Simplified PCNN for Image Segmentation
IEEE Transactions on Neural Networks
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Most of these methods succeeded to achieve the invariance against object translation, rotation and scaling, but that could not neutralize the bright background effect and non-uniform light on the quality of the generated features. To eliminate the limit that the recent subspace learning methods for facial feature extraction are sensitive to the variations of orientation, position and illumination in capturing palmprint images, a novel palmprint feature extraction approach is proposed. Palmprint images are decomposed into a sequence of binary images using a Modified Pulse-Coupled Neural Network (M-PCNN), and then the information entropy of each binary image are calculated and regarded as features. A classifier based Support Vector Machine (SVM) is employed to implement recognition and classification. Simultaneously, it overcomes the disadvantage of standard PCNN model with high number of parameters. Theoretical and experimental results show that the proposed approach is robust to the variations of orientation, position and illumination conditions in comparison with other methods based subspace.