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Ensemble LDA for Face Recognition

  • Hui Kong
  • Xuchun Li
  • Jian-Gang Wang
  • Chandra Kambhamettu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)

Abstract

Linear Discriminant Analysis (LDA) is a popular feature extraction technique for face image recognition and retrieval. However, It often suffers from the small sample size problem when dealing with the high dimensional face data. Two-step LDA (PCA+LDA) [1][2][3] is a class of conventional approaches to address this problem. But in many cases, these LDA classifiers are overfitted to the training set and discard some useful discriminative information. In this paper, by analyzing the overfitting problem for the two-step LDA approach, a framework of Ensemble Linear Discriminant Analysis (E n LDA) is proposed for face recognition with small number of training samples. In E n LDA, a Boosting-LDA (B-LDA) and a Random Sub-feature LDA (RS-LDA) schemes are incorporated together to construct the total weak-LDA classifier ensemble. By combining these weak-LDA classifiers using majority voting method, recognition accuracy can be significantly improved. Extensive experiments on two public face databases verify the superiority of the proposed E n LDA over the state-of-the-art algorithms in recognition accuracy.

Keywords

Training Sample Face Recognition Linear Discriminant Analysis Recognition Accuracy Discriminative Information 
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 2005

Authors and Affiliations

  • Hui Kong
    • 1
  • Xuchun Li
    • 1
  • Jian-Gang Wang
    • 2
  • Chandra Kambhamettu
    • 3
  1. 1.School of Electrical and Electronic EngineeringNanyang Technological UniversitySingapore
  2. 2.Institute for Infocomm ResearchSingapore
  3. 3.Department of Computer and Information ScienceUniversity of DelawareNewark

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