Random Independent Subspace for Face Recognition

  • Jian Cheng
  • Qingshan Liu
  • Hanqing Lu
  • Yen-Wei Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3214)


Independent Component Analysis (ICA) is a popular approach for face recognition. However, face recognition is often a small sample size problem, which will weaken the recognition performance of ICA classifier. In this paper, a novel method is proposed to enhance ICA classifier for the small sample size problem. First, we use the random resampling method to generate some random independent subspaces, and a classifier is constructed in each subspace. Then a voting strategy is adopted to integrate these classifiers for discrimination. Experimental results on public available face database show that the proposed method can obvious improve the performance of ICA classifier.


Face Recognition Independent Component Analysis Face Image Independent Component Analysis Face Database 
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 2004

Authors and Affiliations

  • Jian Cheng
    • 1
  • Qingshan Liu
    • 1
  • Hanqing Lu
    • 1
  • Yen-Wei Chen
    • 2
  1. 1.National Laboratory of Pattern Recognition,Institute of AutomationChinese Academy of SciencesBeijingChina
  2. 2.College of Information Science and EngineeringRitsumeikan UniversityJapan

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