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Learning KPCA for Face Recognition

  • Wangli Hao
  • Jianwu Li
  • Xiao Zhang
Part of the Communications in Computer and Information Science book series (CCIS, volume 375)

Abstract

Kernel principal component analysis (KPCA) is an effective method for face recognition. However, the expression of its final solution needs to take advantage of all training examples, such that its run in real-world application with large scale training set is time-consuming. This paper proposes to apply radial basis function neural network (RBFNN) to learn the feature extraction process of KPCA in order to improve the running efficiency of KPCA-based face recognition system. Experimental results based on two different face benchmark data sets, including ORL and UMIST, show that the proposed method can approach to the recognition accuracy of the original KPCA, but have sparser solutions. The proposed method can be applied to real-time or online face recognition systems.

Keywords

Kernel principal component analysis Radial basis function neural network Face recognition 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Wangli Hao
    • 1
  • Jianwu Li
    • 1
  • Xiao Zhang
    • 1
  1. 1.Beijing Key Lab of Intelligent Information Technology, School of Computer Science and TechnologyBeijing Institute of TechnologyBeijingChina

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