Learned local Gabor patterns for face representation and recognition

  • Authors:

  • Shufu Xie;Shiguang Shan;Xilin Chen;Xin Meng;Wen Gao

  • Affiliations:

  • Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences (CAS), Beijing, 100190, China and Graduate University of CAS, Beijing, 100190, China;Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences (CAS), Beijing, 100190, China;Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences (CAS), Beijing, 100190, China;NEC (China) Co., Ltd., Beijing, 100084, China;Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences (CAS), Beijing, 100190, China and Institute for Digital Media, Peking University, Beij ...

  • Venue:
  • Signal Processing
  • Year:
  • 2009

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Abstract

In this paper, we propose Learned Local Gabor Patterns (LLGP) for face representation and recognition. The proposed method is based on Gabor feature and the concept of texton, and defines the feature cliques which appear frequently in Gabor features as the basic patterns. Different from Local Binary Patterns (LBP) whose patterns are predefined, the local patterns in our approach are learned from the patch set, which is constructed by sampling patches from Gabor filtered face images. Thus, the patterns in our approach are face-specific and desirable for face perception tasks. Based on these learned patterns, each facial image is converted into multiple pattern maps and the block-based histograms of these patterns are concatenated together to form the representation of the face image. In addition, we propose an effective weighting strategy to enhance the performances, which makes use of the discriminative powers of different facial parts as well as different patterns. The proposed approach is evaluated on two face databases: FERET and CAS-PEAL-R1. Extensive experimental results and comparisons with existing methods show the effectiveness of the LLGP representation method and the weighting strategy. Especially, heterogeneous testing results show that the LLGP codebook has very impressive generalizability for unseen data.