The Open Cybernetics & Systemics Journal

2017, 11 : 58-66
Published online 2017 March 29. DOI: 10.2174/1874110X01711010058
Publisher ID: TOCSJ-11-58

RESEARCH ARTICLE
 Palmprint Identification Using Image Reconstruction Based Double DBNs

Xin Pan1, * , Dandan Zhao2 , Tong Chen1 , Jiangping Liu1 , Zhihong Yu3 and Heru Xue1

* 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: , pxffyfx@126.com

ABSTRACT

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.