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Two-Stage Data Reduction for a SVM Classifier in a Face Recognition Algorithm Based on the Active Shape Model

  • Maciej Krol
  • Andrzej Florek
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 95)

Abstract

In this paper, two stage data reduction method for face identification with use of Support Vector Machine (SVM) classifier is evaluated. SVM Classification was performed for data acquired from contour description of 2200 faces of 100 persons. Face contours were extracted from frontal face images with use of Active Shape Model (ASM) method. Two stage PCA+LDA data reduction performance is measured in comparison with single stage PCA or LDA reductions. We propose to replace first stage PCA reduction with much simpler and less computationally intensive contour decimation.

Keywords

Support Vector Machine Active Shape Model Support Vector Machine Training Support Vector Machine Kernel Face Contour 
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 2011

Authors and Affiliations

  • Maciej Krol
    • 1
  • Andrzej Florek
    • 1
  1. 1.Institute of Control and Information EngineeringPoznan University of TechnologyPoznanPoland

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