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Discriminant Analysis of Principal Components for Face Recognition

  • Wenyi Zhao
  • Arvindh Krishnaswamy
  • Rama Chellappa
  • Daniel L. Swets
  • John Weng
Part of the NATO ASI Series book series (volume 163)

Abstract

In this paper we describe a face recognition method based on PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The method consists of two steps: first we project the face image from the original vector space to a face subspace via PCA, second we use LDA to obtain a linear classifier. The basic idea of combining PCA and LDA is to improve the generalization capability of LDA when only few samples per class are available. Using FERET dataset we demonstrate a significant improvement when principal components rather than original images are fed to the LDA classifier. The hybrid classifier using PCA and LDA provides a useful framework for other image recognition tasks as well.

Keywords

Face Recognition Linear Discriminant Analysis Face Image Face Recognition System Automatic Face 
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 1998

Authors and Affiliations

  • Wenyi Zhao
    • 1
  • Arvindh Krishnaswamy
    • 2
  • Rama Chellappa
    • 1
  • Daniel L. Swets
    • 3
  • John Weng
    • 4
  1. 1.Center for Automation ResearchUniversity of MarylandCollege ParkUSA
  2. 2.Electrical Engineering DeptStanford UniversityStanfordUSA
  3. 3.Computer Science DepartmentAugustana CollegeSioux FallsUSA
  4. 4.Computer Science DeptMichigan State UniversityEast LansingUSA

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