Author(s): Xin Pan, Dandan Zhao, Tong Chen, Jiangping Liu, Zhihong Yu, Heru Xue
A novel approach of palmprint recognition using image reconstruction based on double DBNs (IR-DDBN) was proposed in this study, as principal component analysis (PCA) ignores the higher order statistics in feature extraction. Three main steps were involved in the algorithm. Firstly, whitening PCA was utilized to extract the prominent characteristics of the original palmprint image. The second step included reconstructing the original image and calculating the residual image for the residual features between the original and reconstructed images. Finally, the double DBNs were used for classification. The experimental results demonstrated better performance of the proposed algorithm by comparing with the traditional algorithms (PCA, LBP, HOG and DBN) with higher recognition rates, especially for relatively small training samples.
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