Selection and Fusion of Similarity Measure Based Classifiers Using Support Vector Machines

  • Mohammad T. Sadeghi
  • Masoumeh Samiei
  • Josef Kittler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5342)


In this paper, we address the problem of selecting and fusing similarity measures based classifiers in LDA face space. The performance of a face verification system in an LDA feature space using different similarity measure based classifiers is experimentally studied first. The study is performed for both manually and automatically registered face images. A sequential search approach which is in principle similar to the ”plus L and take away R” algorithm is then applied in order to find an optimum subset of the adopted classifiers. The selected classifiers are combined using the SVM classifier. We show that although, individually, one of the adopted scoring functions, the Gradient Direction distance outperforms the other metrics, by fusing different similarity measures using the proposed method, the resulting decision making scheme improves the performance of the system in different conditions.


Support Vector Machine Similarity Measure Linear Discriminant Analysis Gradient Direction Fusion Rule 
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.


  1. 1.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Trans. on Pattern Recognition and Machine Intelligence 19(7), 711–720 (1997)CrossRefGoogle Scholar
  2. 2.
    Devijver, P., Kittler, J.: Pattern Recognition: A Statistical Approach. Prentice-Hall, Englewood Cliffs (1982)zbMATHGoogle Scholar
  3. 3.
    Duin, R.P.W.: The combining classifier: to train or not to train? In: Inter. Conf. on Pattern Recognition (2002)Google Scholar
  4. 4.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55 (August 1997)Google Scholar
  5. 5.
    Hamouz, M., Kittler, J., Kamarainen, J.-K., Paalanen, P., Kalviainen, H., Matas, J.: Feature-based affine-invariant localization of faces. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(9), 1490–1495 (2005)CrossRefGoogle Scholar
  6. 6.
    Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Trans. on Pattern Recognition and Machine Intelligence, PAMI 20(3), 226–239 (1998)CrossRefGoogle Scholar
  7. 7.
    Kittler, J., Roli, F.: Multiple classifier systems, vol. 2096. Springer, Heidelberg (2001)zbMATHGoogle Scholar
  8. 8.
    Maghooli, K., Moin, M.S.: A new approach on multimodal biometrics based on combining neural networks using adaboost. In: ECCV Workshop BioAW (2004)Google Scholar
  9. 9.
    Perlibakas, V.: Distance measures for pca based face recognition. Pattern Recognition letters 25(6) (2004)Google Scholar
  10. 10.
    Pudil, P., Novovicova, J., Kittler, J.: Floating search methods in feature selection. Pattern Recognition Letters 15, 1119–1125 (1994)CrossRefGoogle Scholar
  11. 11.
    Roli, F., Fumera, G.: Analysis of linear and order statistics combiners for fusion of imbalanced classifiers. In: 3rd Int. Workshop on Multiple Classifier Systems, Cagliari, Italy. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  12. 12.
    Sadeghi, M., Kittler, J.: Decision making in the LDA space: Generalised gradient direction metric. In: the 6th International Conference on Automatic Face and Gesture Recognition, Seoul, Korea, pp. 248–253 (May 2004)Google Scholar
  13. 13.
    Sadeghi, M., Kittler, J.: Confidence based gating of multiple face authentication experts. In: Joint IAPR International Workshops on Syntactical and Structural Pattern Recognition and Statistical Pattern Recognition, S+SSPR 2006, Hong Kong, China, August 17-19, pp. 667–676 (2006)Google Scholar
  14. 14.
    Sadeghi, M., Samiei, M., Kittler, J., Almodarresi, M.: Similarity measures fusion using svm classifier for face authentication. In: Proceedings of the third International Conference on Computer Vision Theory and Applications, VISAPP 2008, Portugal, vol. 2, pp. 105–110 (January 2008)Google Scholar
  15. 15.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)CrossRefzbMATHGoogle Scholar
  16. 16.
    Verikas, A., Lipnickas, A., Malmqvist, K., Bacauskiene, M., Gelzinis, A.: Soft combining of neural classifiers: A comparative study. Pattern Recognition Letters 20, 429–444 (1999)CrossRefGoogle Scholar
  17. 17.
    Xu, L., Krzyzak, A., Suen, C.Y.: Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Trans. on Systems, Man, and Cybernetics 22, 418–435 (1992)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Mohammad T. Sadeghi
    • 1
  • Masoumeh Samiei
    • 1
  • Josef Kittler
    • 2
  1. 1.Signal Processing Research Laboratory Department of Electrical and Computer EngineeringYazd UniversityYazdIran
  2. 2.Centre for Vision, Speech and Signal Processing School of Electronics and Physical SciencesUniversity of SurreyGuildfordUK

Personalised recommendations