1 College of Computer and Information Engineering, Inner Mongolia Agricultural University, Huhhot, Inner Mongolia 010018, China
2 Zhengzhou Tourism College, Zhengzhou, Henan 450009, China
3 College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Huhhot, Inner Mongolia 010018, China
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.
Key words: Deep learning, Image reconstruction, Double DBNs, Deep belief nets, Whitening PCA.
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
* Address correspondence to this author at the College of Computer and Information Engineering, Inner Mongolia Agricultural University, 306 Road Zhaowuda, Saihan District, Huhhot , Inner Mongolia, China; Tel: 86-15847129078; E-mails: firstname.lastname@example.org, email@example.com