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)

Abstract

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.

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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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