Ten lectures on wavelets
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Discriminant Waveletfaces and Nearest Feature Classifiers for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
Palmprint recognition using eigenpalms features
Pattern Recognition Letters
Online Palmprint Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fisherpalms based palmprint recognition
Pattern Recognition Letters
A Novel Palmprint Recognition Algorithm Based on PCA&FLD
ICDT '06 Proceedings of the international conference on Digital Telecommunications
Facial Feature Selection Based on SVMs by Regularized Risk Minimization
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Kernel Fisher Discriminant Analysis for Palmprint Recognition
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Biometric Image Discrimination Technologies (Computational Intelligence and Its Applications Series) (Computational Intelligence and Its Applications Series)
Characterization of palmprints by wavelet signatures via directional context modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A face and palmprint recognition approach based on discriminant DCT feature extraction
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Personal recognition using hand shape and texture
IEEE Transactions on Image Processing
Face recognition by applying wavelet subband representation and kernel associative memory
IEEE Transactions on Neural Networks
An improved palmprint recognition system using iris features
Journal of Real-Time Image Processing
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In this paper, we propose an efficient palmprint recognition scheme which has two features: 1) representation of palm images by two dimensional (2-D) wavelet subband coefficients and 2) recognition by a modular, personalized classification method based on Kernel Principal Component Analysis (Kernel PCA). Wavelet subband coefficients can effectively capture substantial palm features while keeping computational complexity low. We then kernel transforms to each possible training palm samples and then mapped the high-dimensional feature space back to input space. Weighted Euclidean linear distance based nearest neighbor classifier is finally employed for recognition. We carried out extensive experiments on PolyU Palmprint database includes 7752 palms from 386 different palms. Detailed comparisons with earlier published results are provided and our proposed method offers better recognition accuracy (99.654%).