The nature of statistical learning theory
The nature of statistical learning theory
The lifting scheme: a construction of second generation wavelets
SIAM Journal on Mathematical Analysis
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
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
Competitive Coding Scheme for Palmprint Verification
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Pattern recognition with SVM and dual-tree complex wavelets
Image and Vision Computing
Palmprint verification based on principal lines
Pattern Recognition
A survey of palmprint recognition
Pattern Recognition
ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging
Knowledge-Based Systems
Palmprint verification based on 2D - Gabor wavelet and pulse-coupled neural network
Knowledge-Based Systems
A face and palmprint recognition approach based on discriminant DCT feature extraction
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nonlinear wavelet transforms for image coding via lifting
IEEE Transactions on Image Processing
A digital architecture for support vector machines: theory, algorithm, and FPGA implementation
IEEE Transactions on Neural Networks
Kerneltron: support vector "machine" in silicon
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
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The recent on-line palmprint recognition algorithms are time-consuming, and not suitable for being implemented with hardware. This paper describes a novel on-line fast palmprint identification approach. In order to reduce the computational cost of extracting palmprint features from a palmprint image and make it easy to be implemented with hardware, we construct an adaptive lifting wavelet scheme to decompose a palmprint image into several subbands, and then the pulse-coupled neural network is employed to decompose each subband into a series of binary images. The entropies of these binary images are calculated and regarded as features. Then, in the classification step, a support vector machine-based classifier is utilized. Experimental results show that the proposed approach yields a better performance in terms of the correct classification percentages compared with the recent on-line palmprint recognition algorithms. It is also shown that the proposed approach yields observably low computational cost and can be easily implemented with hardware.