Bayesian Networks to Combine Intensity and Color Information in Face Recognition

  • Guillaume Heusch
  • Sébastien Marcel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)


We present generative models dedicated to face recognition. Our models consider data extracted from color face images and use Bayesian Networks to model relationships between different observations derived from a single face. Specifically, the use of color as a complementary observation to local, grayscale-based features is investigated. This is done by means of new generative models, combining color and grayscale information in a principled way. Color is either incorporated at the global face level, at the local facial feature level, or at both levels. Experiments on the face authentication task are conducted on two benchmark databases, XM2VTS and BANCA. Obtained results show that integrating color in an intelligent manner improves the performance over a similar baseline system acting on grayscale only, but also over an Eigenfaces-based system were information from different color channels are treated independently.


Bayesian Network Face Recognition Face Image Facial Feature Color Information 
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.
    Russell, R., Sinha, P., Biedermann, I., Nederhouser, M.: Is Pigmentation Important For Face Recognition? Evidence From Contrast Negation. Perception 35, 749–759 (2006)Google Scholar
  2. 2.
    Sinha, P., Balas, B., Ostrovsky, Y., Russel, R.: Face Recognition by Humans: Nineteen Results All Computer Vision Researchers Should Know About. Proceedings of the IEEE, Special Issue on Biometrics: Algorithms and Applications 94(11), 1948–1962 (2006)Google Scholar
  3. 3.
    Torres, L., Reutter, J.Y., Lorente, L.: The Importance of the Color Information in Face Recognition. In: IEEE Intl. Conf. on Image Processing (ICIP), vol. 3, pp. 627–631 (1999)Google Scholar
  4. 4.
    Turk, M., Pentland, A.: Face Recognition Using Eigenfaces. In: IEEE Intl. Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 586–591 (1991)Google Scholar
  5. 5.
    Gutta, S., Huang, J., Chengjun, L., Wechsler, H.: Comparative Performance Evaluation of Gray-Scale and Color Information for Face Recognition Tasks. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 38–43. Springer, Heidelberg (2001)Google Scholar
  6. 6.
    Sadeghi, M., Khoshrou, S., Kittler, J.: SVM-Based Selection of Colour Space Experts for Face Authentication. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 907–916. Springer, Heidelberg (2007)Google Scholar
  7. 7.
    Jones, C.I., Abott, A.L.: Color Face Recognition by Hypercomplex Gabor Analysis. In: IEEE Intl. Conf. on Automatic Face and Gesture Recognition (AFGR), pp. 126–131 (2006)Google Scholar
  8. 8.
    Heusch, G., Marcel, S.: Face Authentication with Salient Facial Features and Static Bayesian Network. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 878–887. Springer, Heidelberg (2007)Google Scholar
  9. 9.
    Messer, K., Matas, J., Kittler, J., Lüttin, J., Maitre, G.: XM2VTSDB: The Extended M2VTS Database. In: Intl. Conf. Audio- and Video-based Biometric Person Authentication (AVBPA), pp. 72–77 (1999)Google Scholar
  10. 10.
    Bailly-Baillière, E., et al.: The Banca Database and Evaluation Protocol. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 625–638. Springer, Heidelberg (2003)Google Scholar
  11. 11.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco (1988)Google Scholar
  12. 12.
    Cowell, G., Dawid, P., Lauritzen, L., Spiegelhalter, J.: Probabilistic Networks and Expert Systems. Springer, Heidelberg (1999)Google Scholar
  13. 13.
    Dempster, A., Laird, N., Rubin, D.: Maximum Likelihood From Incomplete Data via the EM Algorithm. The Journal of Royal Statistical Society 39, 1–37 (1977)Google Scholar
  14. 14.
    Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active Shape Models: Their Training and Applications. Computer Vision and Image Understanding 61(1), 38–59 (1995)Google Scholar
  15. 15.
    Gauvain, J.L., Lee, C.H.: Maximum A Posteriori Estimation for Multivariate Gaussian Mixture Observations of Markov Chains. IEEE Trans. on Speech and Audio Processing 2(2), 291–298 (1994)Google Scholar
  16. 16.
    Cardinaux, F., Sanderson, C., Bengio, S.: User Authentication via Adapted Statistical Models of Face Images. IEEE Trans. on Signal Processing 54(1), 361–373 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Guillaume Heusch
    • 1
    • 2
  • Sébastien Marcel
    • 1
  1. 1.Centre du ParcIdiap Research InstituteMartignySwitzerland
  2. 2.Ecole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland

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