Comparison of Novel Dimension Reduction Methods in Face Verification

  • Licesio J. Rodríguez-Aragón
  • Cristina Conde
  • Enrique Cabello
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4142)


The problem of high dimensionality in face verification tasks has recently been simplified by the use of underlying spatial structures as proposed in the Two Dimensional Principal Component Analysis, the Two Dimensional Linear Discriminant Analysis and the Coupled Subspaces Analysis. Besides, the Small Sample Size problem that caused serious difficulties in traditional LDA has been overcome by the spatial approach 2DLDA. 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: illumination, pose, expression or occlusion. For training the SVMs, only two images per subject have been provided to fit in the small sample size problem.


Feature Vector Linear Discriminant Analysis Training Image Projected Vector Dimension Reduction Method 
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
  • Enrique Cabello
    • 1
  1. 1.Universidad Rey Juan CarlosMóstoles, MadridSpain

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