Class Specific Threshold Selection in Face Space Using Set Estimation Technique for RGB Color Components

  • Madhura Datta
  • C. A. Murthy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5909)

Abstract

In conventional face recognition techniques, any query image is always classified to one of the face classes irrespective of whether the query image is a face or not. Most of the recognition algorithms are dissimilarity based, and one needs to put a proper threshold on the dissimilarity value for the sake of classification. In this paper, we have introduced a novel thresholding technique for classification, where the query image is not necessarily classified to one of the classes in the training set. The theoretical formulation of the thresholding technique and its utility are demonstrated on color face and non face datasets with RGB color components as features in the subspace. The proposed threshold selection is based on statistical method of set estimation and is guided by minimal spanning tree. Experiment shows that the proposed class specific threshold based technique performs better than the non threshold based systems using subspace algorithms.

Keywords

Feature extraction Color components Minimal spanning tree Intra-class threshold Set estimation 

References

  1. 1.
    Solar, J.R.D., Navarrete, P.: Eigenspace-based face recognition: A comparative study of different approaches. IEEE Transactions on Systems, Man and Cybernetics, Part C 35(3), 315–325 (2005)CrossRefGoogle Scholar
  2. 2.
    Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, P.: Face recognition: A literature survey. ACM Computing Surveys, 399–458 (2003)Google Scholar
  3. 3.
    Shakhnarovich, G., Moghaddam, B.: Face Recognition in Subspaces. In: Li, S.Z., Jain, A.K. (eds.) Handbook of Face Recognition. Springer, Heidelberg (2004)Google Scholar
  4. 4.
    Mansfield, A.J., Wayman, J.L.: Best practices in testing and reporting performance of biometric devices, version 2.01. Centre for Mathematics and Scientific Computing, National Physical Laboratory,middlesex (August 2002)Google Scholar
  5. 5.
    Martin, A., Doddington, G., Kamm, T., Ordowski, M.: The det curve in assessment of detection task performance. In: Proc. of Eurospeech 1997, vol. 4, pp. 1895–1898 (1997)Google Scholar
  6. 6.
    Edelsbrunner, H., Kirkpatrick, D., Seidel, R.: On the shape of a set of points in a plane. IEEE Trans. on Inform. Theory IT-29, 551–559 (1983)Google Scholar
  7. 7.
    Mandal, D.P., Murthy, C.A., Pal, S.K.: Determining the shape of a pattern class from sampled points: Extension to rn. Int. J. of General Systems 26(4), 293–320 (1997)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Torres, L., Reuttter, J.Y., Lorente, L.: The importance of the color information in face recognition. In: Proceedings of IEEE Int. Conf. on Image Processing Cobe, Japan, pp. 25–29 (1999)Google Scholar
  9. 9.
    Murthy, C.A.: On consistent estimation of classes in the context of cluster analysis (1988)Google Scholar
  10. 10.
    Grenander, U.: Abstract inference. John Wiley, New York (1981)MATHGoogle Scholar
  11. 11.
    Martinez, A., Benavente, R.: The ar face database. CVC Technical report 24 (June 1998)Google Scholar
  12. 12.
    Nene, S.A., Nayar, S.K., Murase, H.: Columbia object image library (coil-100). Technical report cucs-006-96 (February 1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Madhura Datta
    • 1
  • C. A. Murthy
    • 2
  1. 1.UGC-Academic Staff CollegeUniversity of CalcuttaKolkataIndia
  2. 2.Machine Intelligence UnitIndian Statistical InstituteKolkataIndia

Personalised recommendations