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Comparing and Combining Spatial Dimension Reduction Methods in Face Verification

  • Licesio J. Rodríguez-Aragón
  • Cristina Conde
  • Ángel Serrano
  • Enrique Cabello
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)

Abstract

The problem of high dimensionality in face verification tasks has recently been simplified by the use of underlying spatial structures as proposed in the 2DPCA, 2DLDA and CSA methods. Fusion techniques at both levels, feature extraction and matching score, have been developed to join the information obtained and achieve better results in verification process. The application of these advances to facial verification techniques using different SVM schemes as classification algorithm is here shown. The experiments have been performed over a wide facial database (FRAV2D including 109 subjects), in which only one interest variable was changed in each experiment. For training the SVMs, only two images per subject have been provided to fit in the small sample size problem.

Keywords

Feature Vector Feature Extraction Linear Discriminant Analysis Equal Error Rate Projected Vector 
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

  • Licesio J. Rodríguez-Aragón
    • 1
  • Cristina Conde
    • 1
  • Ángel Serrano
    • 1
  • Enrique Cabello
    • 1
  1. 1.Universidad Rey Juan CarlosMóstoles, MadridSpain

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