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Journal of Electrical and Computer Engineering
Volume 2016 (2016), Article ID 6423834, 7 pages
Research Article

A Novel Dictionary Learning Model with PT-HLBP for Palmprint Recognition

1School of Information Science and Engineering, Shandong University, Jinan 250100, China
2School of Information Science and Engineering, Shandong Agricultural University, Taian 271018, China

Received 14 June 2016; Revised 12 October 2016; Accepted 18 October 2016

Academic Editor: Spyros Tragoudas

Copyright © 2016 Xiumei Guo and Weidong Zhou. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A novel projective dictionary pair learning (PDPL) model with statistical local features for palmprint recognition is proposed. Pooling technique is used to enhance the invariance of hierarchical local binary pattern (PT-HLBP) for palmprint feature extraction. PDPL is employed to learn an analysis dictionary and a synthesis dictionary which are utilized for image discrimination and representation. The proposed algorithm has been tested by the Hong Kong Polytechnic University (PolyU) database (v2) and ideal recognition accuracy can be achieved. Experimental results indicate that the algorithm not only greatly reduces the time complexity in training and testing phase, but also exhibits good robustness for image rotation and corrosion.