Kernel Sparse Representation for Image Classification and Face Recognition

  • Shenghua Gao
  • Ivor Wai-Hung Tsang
  • Liang-Tien Chia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6314)


Recent research has shown the effectiveness of using sparse coding(Sc) to solve many computer vision problems. Motivated by the fact that kernel trick can capture the nonlinear similarity of features, which may reduce the feature quantization error and boost the sparse coding performance, we propose Kernel Sparse Representation(KSR). KSR is essentially the sparse coding technique in a high dimensional feature space mapped by implicit mapping function. We apply KSR to both image classification and face recognition. By incorporating KSR into Spatial Pyramid Matching(SPM), we propose KSRSPM for image classification. KSRSPM can further reduce the information loss in feature quantization step compared with Spatial Pyramid Matching using Sparse Coding(ScSPM). KSRSPM can be both regarded as the generalization of Efficient Match Kernel(EMK) and an extension of ScSPM. Compared with sparse coding, KSR can learn more discriminative sparse codes for face recognition. Extensive experimental results show that KSR outperforms sparse coding and EMK, and achieves state-of-the-art performance for image classification and face recognition on publicly available datasets.


Face Recognition Visual Word Reconstruction Error Sparse Code High Dimensional Feature Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Shenghua Gao
    • 1
  • Ivor Wai-Hung Tsang
    • 1
  • Liang-Tien Chia
    • 1
  1. 1.School of Computer EngineeringNanyang Technological UnivertiySingapore

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