LBP-based Hierarchical Sparse Patch Learning for Face Recognition

نویسندگان

  • Yue Zhao
  • Jianbo Su
چکیده

Local Binary Pattern (LBP) features and its variants are computed on the patches with the fixed positions and a fixed size in images, while the limited variety of the size and position cannot accurately measure the nature of face image. In this paper, we propose a new learning method, Hierarchical Sparse Patch Learning (HSPL), to select face patches with different positions and sizes for face recognition. HSPL employs a sparse learning model to hierarchically select patches at two levels: in level 1 the optimal patch candidates are figured out, while in level 2 the optimal patches from the candidates are obtained. LBP features are extracted from the optimal patches to recognize faces. Experimental results show that the proposed method is more efficient and achieves higher recognition rate than the other two compared methods.

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تاریخ انتشار 2013