Signal, Image and Video Processing

, Volume 12, Issue 6, pp 1035–1042 | Cite as

Combining Modified LBP and Weighted SRC for Palmprint Recognition

  • Shanwen Zhang
  • Haoxiang WangEmail author
  • Wenzhun Huang
  • Chuanlei Zhang
Original Paper


In order to extract invariant features in the palmprint transformation of scale, rotation and affine distortion, a coarse-to-fine palmprint recognition method is proposed by combining the weighted adaptive center symmetric local binary pattern (WACS-LBP) and weighted sparse representation based classification (WSRC). The method consists of coarse and fine stages. In the coarse stage, using the similarity between the test sample and one sample of each training class, most of the training classes could be excluded and a small number of candidate classes of the test sample are reserved. Thus, the original classification problem becomes clear and simple. In the fine stage, the robust rotation invariant weighted histogram feature vector is extracted from each candidate sample and the test sample by WACS-LBP, and the weighted sparse representation optimal problem is constructed by the similarity between the test sample and each candidate training sample, and the test sample is recognized by the minimum residual. The proposed method is tested and compared with the existing algorithms on the PolyU and CASIA database. The experimental results illustrate better performance and rationale interpretation of the proposed method.


Palmprint recognition Weighted adaptive center symmetric local binary pattern (WACS-LBP) Sparse representation based classification (SRC) Weighted SRC (WSRC) 



This work is partially supported by the China National Natural Science Foundation under grant Nos. 61473237. The authors would like to thank all the editors and anonymous reviewers for their constructive advices. The authors would like to thank the Hong Kong Polytechnic University and Chinese Academy of Sciences Institute of Automation for sharing their palmprint database with us.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Shanwen Zhang
    • 1
  • Haoxiang Wang
    • 2
    Email author
  • Wenzhun Huang
    • 1
  • Chuanlei Zhang
    • 1
  1. 1.Department of Information EngineeringXijing UniversityXi’anChina
  2. 2.Cornell UniversityIthacaUSA

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