Advertisement

A Parallel Implementation of Error Correction SVM with Applications to Face Recognition

  • Qingshan Yang
  • Chengan Guo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5552)

Abstract

The Error Correction SVM method is an excellent multiclass classification approach and has been applied to face recognition successfully. Yet, it suffers from the computational complexity. To reduce the computation time of the algorithm, a parallel implementation scheme is presented in the paper in which the training and classification tasks are assigned to multiple processors and run on all the processors simultaneously. The simulation experiments conducted on a local area network using Cambridge ORL face database show that the parallel algorithm given in the paper is effective in speeding up the algorithms of the training and classification while maintaining the recognition accuracy unchanged.

Keywords

Face recognition Parallel algorithm Error Correction SVM 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face Recognition: A Literature Survey. ACM Computing Surveys 35(4), 399–458 (2003)CrossRefGoogle Scholar
  2. 2.
    Asanovic, K., Bodik, R., et al.: The Landscape of Parallel Computing Research: A View from Berkly. Technical report, University of California, Berkeley (2006)Google Scholar
  3. 3.
    Vapnik, V.: Statistical Learning Theory. John Willey and Sons Inc., New York (1998)zbMATHGoogle Scholar
  4. 4.
    Sebald, D.J., Bucklew, J.A.: Support Vector Machines and Multiple Hypothesis Test Problem. IEEE Trans. on Signal Processing 49(11), 2865–2872 (2001)CrossRefGoogle Scholar
  5. 5.
    Kreβel, U.: Pairwise Classification and Support Vector Machines. In: Schölkopr, B., Burges, J.C., Smola, A.J. (eds.) Advances in Kernel Methods: Support Vector Learning. MIT Press, Cambridge (1999)Google Scholar
  6. 6.
    Wang, C., Guo, C.: An SVM Classification Algorithm with Error Correction Ability Applied to Face Recognition. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3971, pp. 1057–1062. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Guo, C., Yuan, C., Ma, H.: A Two-Pass Classification Method Based on Hyper-ellipsoid Neural Networks and SVM’s with Applications to Face Recognition. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds.) ISNN 2007. LNCS, vol. 4493, pp. 461–468. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  8. 8.
    Collobert, R., Bengio, S.: A Parallel Mixture of SVMs for Very Large Scale Problems. Neural Computation 14(5), 1105–1114 (2002)CrossRefzbMATHGoogle Scholar
  9. 9.
    Zanghirati, G., Zanni, L.: A parallel solver for large quadratic programs in training support vector machines. Parallel Comput. 14(4), 535–551 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Cao, L.J., Keerthi, S.S., et al.: Parallel Sequential Minimal Optimization for the Training of Support Vector Machines. IEEE Trans. on Neural Networks 17(4), 1039–1049 (2006)CrossRefGoogle Scholar
  11. 11.
    Lin, S., Costello, D.J.: Error Control Coding: Fundamentals and Applications. Prentice-Hall, Inc., Englewood Cliffs (1983)zbMATHGoogle Scholar
  12. 12.
    Keerthi, S.S., Shevade, S.K., et al.: Improvements to Platt’s SMO Algorithm for SVM Classifier Design. Neural Computation 13, 637–649 (2001)CrossRefzbMATHGoogle Scholar
  13. 13.
    Liu, C., Wechsler, H.: Gabor Feature Based Classification Using the Enhanced Fisher Linear Discrimination Model for Face Recognition. IEEE Trans. on Image Processing 11(4), 467–476 (2002)CrossRefGoogle Scholar
  14. 14.
    Xie, X., Lam, K.: Gabor-based Kernel PCA with Doubly Nonlinear Mapping for Face Recognition with a Single Face Image. IEEE Trans. on Image Processing 15(9), 2481–2492 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Qingshan Yang
    • 1
  • Chengan Guo
    • 1
  1. 1.School of Electronic and Information EngineeringDalian University of Technology,DalianLiaoningChina

Personalised recommendations