Ten lectures on wavelets
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
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
Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Independent component analysis of Gabor features for face recognition
IEEE Transactions on Neural Networks
Stockwell transform based palm-print recognition
Applied Soft Computing
Palmprint based recognition system using phase-difference information
Future Generation Computer Systems
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This paper presents Gabor-based kernel Principal Component Analysis (KPCA) method by integrating the Gabor wavelet and the KPCA methods for palmprint recognition. The intensity values of the palmprint images extracted by using an image preprocessing method are first normalized. Then Gabor wavelets are applied to derive desirable palmprint features. The transformed palm images exhibit strong characteristics of spatial locality, scale, and orientation selectivity. The KPCA method nonlinearly maps the Gabor wavelet image into a high-dimensional feature space and the matching is realized by weighted Euclidean distance. The proposed algorithm has been successfully tested on the PolyU palmprint database which the samples were collected in two different sessions. Experimental results show that this method achieves 97.22% accuracy for PolyU dataset using 3850 images from 385 different palms captured in the first session as train set and the second session im0061ges as test set.