Face Verification Advances Using Spatial Dimension Reduction Methods: 2DPCA & SVM

  • Licesio J. Rodríguez-Aragón
  • Cristina Conde
  • Ángel Serrano
  • Enrique Cabello
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)


Spatial dimension reduction called Two Dimensional PCA method has recently been presented. The application of this variation of traditional PCA considers images as 2D matrices instead of 1D vectors as other dimension reduction methods have been using. The application of these advances to verification techniques, using SVM as classification algorithm, is here shown. The simulation has been performed over a complete facial images database called FRAV2D that contains different sets of images to measure the improvements on several difficulties such as rotations, illumination problems, gestures or occlusion.

The new method endowed with a classification strategy of SVMs, seriously improves the results achieved by the traditional classification of PCA & SVM.


Principal Component Analysis Feature Vector Independent Component Analysis Independent Component Analysis Equal Error Rate 
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 2005

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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