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Improved Face Recognition Using Extended Modular Principal Component Analysis

  • Changhan Park
  • Inho Paek
  • Joonki Paik
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4291)

Abstract

In this paper, we present an improved face recognition algorithm using extended modular principal component analysis (PCA). The proposed method, when compared with a regular PCA-based algorithm, has significantly improved recognition rate with large variations in pose, lighting direction, and facial expression. The face images are divided into multiple, smaller blocks based on the Gaussian model and we use the PCA approach to these combined blocks for obtaining two eyes, nose, mouth, and glabella. Priority for merging blocks is decided by using fuzzy logic. Some of the local facial features do not vary with pose, lighting direction, and facial expression. The proposed technique is robust against these variations.

Keywords

Principal Component Analysis Facial Expression Face Recognition Singular Value Decomposition Face Image 
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 2006

Authors and Affiliations

  • Changhan Park
    • 1
    • 2
  • Inho Paek
    • 1
  • Joonki Paik
    • 1
  1. 1.Image Processing and Intelligent Systems Laboratory, Department of Image Engineering, Graduate School of Advanced Imaging Science, Multimedia, and FilmChung-Ang UniversitySeoulKorea
  2. 2.Advanced Technology R&D CenterSamsung Thales Co., Ltd.GyeonggiKorea

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