ICIP 2006, Atlanta, GA
 

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Paper Detail

Paper:WA-P6.1
Session:Biometrics
Time:Wednesday, October 11, 09:40 - 12:20
Presentation: Poster
Topic: Biometrics: Other
Title: PALMPRINT CLASSIFICATION USING DUAL-TREE COMPLEX WAVELETS
Authors: Guangyi Chen; Concordia University 
 T. D. Bui; Concordia University 
 A. Krzyzak; Concordia University 
Abstract: A new palmprint classification method is proposed in this paper by using the dual-tree complex wavelet transform. The dual-tree complex wavelet transform has such important properties as the approximate shift-invariance and high directional selectivity. These properties are very important in invariant palmprint classification. Support vector machines are used as a classifier and the Gaussian radial basis function kernel is selected in the experiments. Experimental results show that the dual-tree complex wavelet features outperform the scalar wavelet features, and three previously developed methods. We conclude that the dual-tree complex wavelet features should be used for invariant palmprint classification instead of the scalar wavelet features.