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Technical Program
Paper Detail
| Paper: | WA-P6.1 |
| Session: | Biometrics |
| Time: | Wednesday, October 11, 09:40 - 12:20 |
| Presentation: |
Poster
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| 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. |
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