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Learning Multi-scale Block Local Binary Patterns for Face Recognition

  • Shengcai Liao
  • Xiangxin Zhu
  • Zhen Lei
  • Lun Zhang
  • Stan Z. Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)

Abstract

In this paper, we propose a novel representation, called Multi-scale Block Local Binary Pattern (MB-LBP), and apply it to face recognition. The Local Binary Pattern (LBP) has been proved to be effective for image representation, but it is too local to be robust. In MB-LBP, the computation is done based on average values of block subregions, instead of individual pixels. In this way, MB-LBP code presents several advantages: (1) It is more robust than LBP; (2) it encodes not only microstructures but also macrostructures of image patterns, and hence provides a more complete image representation than the basic LBP operator; and (3) MB-LBP can be computed very efficiently using integral images. Furthermore, in order to reflect the uniform appearance of MB-LBP, we redefine the uniform patterns via statistical analysis. Finally, AdaBoost learning is applied to select most effective uniform MB-LBP features and construct face classifiers. Experiments on Face Recognition Grand Challenge (FRGC) ver2.0 database show that the proposed MB-LBP method significantly outperforms other LBP based face recognition algorithms.

Keywords

LBP MB-LBP Face Recognition AdaBoost 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Shengcai Liao
    • 1
  • Xiangxin Zhu
    • 1
  • Zhen Lei
    • 1
  • Lun Zhang
    • 1
  • Stan Z. Li
    • 1
  1. 1.Center for Biometrics and Security Research &, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun Donglu, Beijing 100080China

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