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 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neurosicience 3, 71–86 (1999)CrossRefGoogle Scholar
  2. 2.
    Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 711–720 (1997)CrossRefGoogle Scholar
  3. 3.
    Guo, G., Dyer, C.: Learning from examples in the small sample case: face expression recognition. IEEE Transactions on Systems, Man, and Cybernetics-Part B 35, 477–488 (2005)CrossRefGoogle Scholar
  4. 4.
    Pang, S., Kim, D., Bang, S.Y.: Memebership authentication in the dynamic group by face classification using SVM ensemble. Pattern Recognition Letters 24, 215–225 (2003)MATHCrossRefGoogle Scholar
  5. 5.
    Cao, L., Chong, W.: Feature extraction in support vector machine: A comparison of PCA, KPCA and ICA. In: Proceedings of the 9th International Conference on Neural Information Processing (ICONIP’OZ), vol. 2, pp. 1001–1005 (2002)Google Scholar
  6. 6.
    Yang, J., Yang, J.: From image vector to matrix: a straightforward image projection technique–IMPCA vs. PCA. Pattern Recognition 35, 1997–1999 (2002)MATHCrossRefGoogle Scholar
  7. 7.
    Yang, J., Zhang, D., Frangi, F., Yang, J.: Two-dimmensional PCA: A new approach to apperance-based face representation and recognition. IEEE Transacctions on Pattern Recognition and Machine Intelligence 26, 131–137 (2004)CrossRefGoogle Scholar
  8. 8.
    Li, M., Yuan, B.: A novel statistical linear discriminant analysis for image matrix: two-dimensional fisherfaces. In: Proceedings of the International Conference on Signal Processing, pp. 1419–1422 (2004)Google Scholar
  9. 9.
    Chen, S., Zhu, Y., Zhang, D., Yang, J.: Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA. Pattern Recognition Letters 26, 1157–1167 (2005)CrossRefGoogle Scholar
  10. 10.
    Xu, D., Yan, S., Zhang, L., Liu, Z., Zhang, H.: Coupled subspace analysis. Technical Report MSR-TR-2004-106, Microsof Research (2004)Google Scholar
  11. 11.
    Cortes, C., Vapnik, V.: Support vector network. Machine Learning 20, 273–297 (1995)MATHGoogle Scholar
  12. 12.
    Fortuna, J., Capson, D.: Improved support vector classification using PCA and ICA feature space modiffication. Pattern Recognition 37, 1117–1129 (2004)MATHCrossRefGoogle Scholar
  13. 13.
    Joachims, T.: Making large scale support vector machine learning practical. In: Advances in Kernel Methods: Support Vector Machines. MIT Press, Cambridge (1998)Google Scholar
  14. 14.
    Yan, S., Xu, D., Yang, Q., Zhang, L., Tang, X., Zhang, H.: Discriminant analysis with tensor representation. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, p. 526–532 (2005)Google Scholar

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

Personalised recommendations