Advertisement

Optimization of Face Relevance Maps with Total Classification Error Minimization

  • Michal Kawulok
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5112)

Abstract

This paper presents a concept of optimizing parameters used for solving image identification tasks developed during research aimed at improving recognition of human face images. Effectiveness of closed-set identification is measured in a form of Total Classification Error (TCE) which can be expressed as a function of parameters used for calculating similarity between samples. TCE can be minimized for a defined training set in order to obtain optimal values of the parameters. This method was implemented to optimize face relevance maps applied to improve the Eigenfaces method for human face recognition. Results of the experiments presented in this paper confirm effectiveness of the developed approach.

Keywords

Feature Vector Face Recognition Face Image Face Recognition System Partial Error 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 711–720 (1997)CrossRefGoogle Scholar
  2. 2.
    Daugman, J.: How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology 14(1), 21–31 (2004)CrossRefGoogle Scholar
  3. 3.
    Gong, S., McKenna, S.J., Psarrou, A.: Dynamic Vision From Images to Face Recognition. Imperial College Press (1999)Google Scholar
  4. 4.
    Grother, P., Micheals, R., Phillips, P.J.: Face recognition vendor test 2002 performance metrics. In: Proceedings of the Fourth International Conference on Audio-Visual Based Person Authentication (2003)Google Scholar
  5. 5.
    Hsu, R.-L., Abdel-Mottaleb, M., Jain, A.K.: Face detection in color images. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 696–706 (2002)CrossRefGoogle Scholar
  6. 6.
    Kawulok, M., Smolka, B.: Application of color information in human face recognition. Medical Informatics & Technologies MIT 2006, 395–400 (2006)Google Scholar
  7. 7.
    Kawulok, M., Smolka, B.: Improvement of face recognition effectiveness based on color information. In: Proceedings of 13th International Conference on Systems, Signals and Image Processing (IWSSIP 2006), Budapest, Hungary, pp. 69–73 (2006)Google Scholar
  8. 8.
    Kawulok, M.: Masks and eigenvectors weights for Eigenfaces method improvement. In: Wojciechowski, K., et al. (eds.) Proceedings of Computer Vision and Graphics International Conference, ICCVG 2004, pp. 528–533. Springer, Berlin (2004)Google Scholar
  9. 9.
    Kawulok, M.: Selected methods of improving automatic face recognition effectiveness (Wybrane metody poprawy skutecznosci automatycznego rozpoznawania obrazow twarzy), PhD Thesis, Silesian University of Technology, Gliwice (2006)Google Scholar
  10. 10.
    Phillips, P.J., Grother, P.J., Micheals, R.J., Blackburn, D.M., Tabassi, E., Bone, J.M.: Face Recognition Vendor Test 2002: Evaluation Report. NISTIR 6965 (2003)Google Scholar
  11. 11.
    Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the Face Recognition Grand Challenge. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 947–954 (2005)Google Scholar
  12. 12.
    Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.: The FERET database and evaluation procedure for face recognition algorithms. Image and Vision Computing J. 16(5), 295–306 (1998)CrossRefGoogle Scholar
  13. 13.
    Ramachandran, M., Zhou, S.K., Jhalani, D., Chellappa, R.: A method for converting a smiling face to a neutral face with applications to face recognition. In: IEEE International Conference on Acoustics, Speech and Signal Processing (2005)Google Scholar
  14. 14.
    Turk, M., Pentland, A.: Face Recognition Using Eigenfaces. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 586–591 (1991)Google Scholar
  15. 15.
    Wechsler, H., Phillips, P.J., Bruce, V., Soulie, F.F., Huang, T.S.: Face Recognition: From Theory to Applications. Springer, Berlin (1998)MATHGoogle Scholar
  16. 16.
    Wilson, C., Austin Hicklin, R., Korves, H., Ulery, B., Zoepfl, M., Bone, M., Grother, P., Micheals, R., Otto, S., Watson, C.: Fingerprint Vendor Technology Evaluation 2003. Analysis report. Technical Report NISTIR 7123, National Institute of Standards and Technology (2003)Google Scholar
  17. 17.
    Wiskott, L., Fellous, J.M., Kruger, N., Malsburg, C.: Face recognition by Elastic Bunch Graph Matching. Technical Report IR-INI 96-08, Ruhr-Universitat Bochum, Germany (1996)Google Scholar
  18. 18.
    Yambor, W., Draper, B., Beveridge, R.: Analyzing PCA-based face recognition algorithms: Eigenvector selection and distance measures. Empirical Evaluation Methods in Computer Vision (2002)Google Scholar
  19. 19.
    Xu, Z.-W., Guo, X.-X., Hu, X.-Y., Cheng, X.: The blood vessel recognition of ocular fundus. In: Proceedings of 2005 International Conference on Machine Learning and Cybernetics, vol. 7, pp. 4493–4498 (2005)Google Scholar
  20. 20.
    Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: A literature survey. Technical Report CARTR-948, Center for Automation Research, University of Maryland, College Park (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Michal Kawulok
    • 1
  1. 1.Institute of Computer ScienceSilesian University of TechnologyGliwicePoland

Personalised recommendations