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Pattern Recognition and Image Analysis

, Volume 20, Issue 4, pp 513–527 | Cite as

Application of two-dimensional principal component analysis for recognition of face images

  • N. L. ShchegolevaEmail author
  • G. A. Kukharev
Representation, Processing, Analysis, and Understanding of Images

Abstract

A two-dimensional principal component analysis (2D PCA) method directed at processing digital images is discussed. The method is based on representation of images as a set of rows and columns analyzing these sets. Two methods of realizing the 2D PCA corresponding to the parallel and cascade forms of its realization are presented, and their characteristics are estimated. The application of the 2D PCA method is shown for solving problems of representation and recognition of facial images. The experiments are fulfilled on ORL and FERET bases.

Keywords

facial images recognition Two-Dimensional Principal Component Analysis (2D PCA) parallel and cascade forms of 2D PCA realization reduction of attribute space dimension reduction of operational complexity 2D PCA application 

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Copyright information

© Pleiades Publishing, Ltd. 2010

Authors and Affiliations

  1. 1.Saint Petersburg State Electrotechnical UniversitySaint PetersburgRussia

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