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Local and Global Feature Extraction for Face Recognition

  • Yongjin Lee
  • Kyunghee Lee
  • Sungbum Pan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3546)

Abstract

This paper proposes a new feature extraction method for face recognition. The proposed method is based on Local Feature Analysis (LFA). LFA is known as a local method for face recognition since it constructs kernels which detect local structures of a face. It, however, addresses only image representation and has a problem for recognition. In the paper, we point out the problem of LFA and propose a new feature extraction method by modifying LFA. Our method consists of three steps. After extracting local structures using LFA, we construct a subset of kernels, which is efficient for recognition. Then we combine the local structures to represent them in a more compact form. This results in new bases which have compromised aspects between kernels of LFA and eigenfaces for face images. Through face recognition experiments, we verify the efficiency of our method.

Keywords

Feature Extraction Face Recognition Face Image Reconstruction Error Feature Extraction Method 
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

  • Yongjin Lee
    • 1
  • Kyunghee Lee
    • 2
  • Sungbum Pan
    • 3
  1. 1.Biometrics Technology Research TeamElectronics and Telecommunications Research InstituteDaejeonKorea
  2. 2.Department of Electrical EngineeringThe University of SuwonKorea
  3. 3.Division of Information and Control Measurement EngineeringChosun UniversityKorea

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