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Face Recognition under Variant Illumination Using PCA and Wavelets

  • Mong-Shu Lee
  • Mu-Yen Chen
  • Fu-Sen Lin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5575)

Abstract

In this paper, an efficient wavelet subband representation method is proposed for face identification under varying illumination. In our presented method, prior to the traditional principal component analysis (PCA), we use wavelet transform to decompose the image into different frequency subbands, and a low-frequency subband with three secondary high-frequency subbands are used for PCA representations. Our aim is to compensate for the traditional wavelet-based methods by only selecting the most discriminating subband and neglecting the scattered characteristic of discriminating features. The proposed algorithm has been evaluated on the Yale Face Database B. Significant performance gains are attained.

Keywords

Face recognition Principal component analysis Wavelet transform Illumination 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Mong-Shu Lee
    • 1
  • Mu-Yen Chen
    • 1
  • Fu-Sen Lin
    • 1
  1. 1.Department of Computer Science and EngineeringNational Taiwan Ocean UniversityKeelungTaiwan

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