The multiscale competitive code via sparse representation for palmprint verification

Wangmeng Zuo, Zhouchen Lin, Zhenhua Guo, Dapeng Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

71 Citations (Scopus)

Abstract

Palm lines are the most important features for palmprint recognition. They are best considered as typical multiscale features, where the principal lines can be represented at a larger scale while the wrinkles at a smaller scale. Motivated by the success of coding-based palmprint recognition methods, this paper investigates a compact representation of multiscale palm line orientation features, and proposes a novel method called the sparse multiscale competitive code (SMCC). The SMCC method first defines a filter bank of second derivatives of Gaussians with different orientations and scales, and then uses the l1-norm sparse coding to obtain a robust estimation of the multiscale orientation field. Finally, a generalized competitive code is used to encode the dominant orientation. Experimental results show that the SMCC achieves higher verification accuracy than state-of-the-art palmprint recognition methods, yet uses a smaller template size than other multiscale methods.
Original languageEnglish
Title of host publication2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010
Pages2265-2272
Number of pages8
DOIs
Publication statusPublished - 31 Aug 2010
Event2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010 - San Francisco, CA, United States
Duration: 13 Jun 201018 Jun 2010

Conference

Conference2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010
Country/TerritoryUnited States
CitySan Francisco, CA
Period13/06/1018/06/10

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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