An Improved Principal Component Analysis for Palmprint Recognition

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In this paper, an improved principal component analysis method of is proposed. In this approach, firstly, palmprint features are extracted by the Gabor wavelets. Then, the bi-directional compressed 2DPCA is applied to remove redundant information from the image rows and columns. Finally, principal component analysis is used to further reduce the dimension. The proposed method has greater palmprint recognition accuracy at the same time reducing the dimension. Experiment results show that the effectiveness of the proposed method is verified using the PolyU palmprint databases.
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2DPCA; Gabor Wavelets; Palmprint Feature; Principal Component Analysis

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