Modeling Phase Spectra Using Gaussian Mixture Models for Human Face Identification

  • Sinjini Mitra
  • Marios Savvides
  • Anthony Brockwell
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3687)


It has been established that information distinguishing one human face from another is contained to a large extent in the Fourier domain phase component of the facial image. However, to date, formal statistical models for this component have not been deployed in face recognition tasks. In this paper we introduce a model-based approach using Gaussian mixture models (GMM) for the phase component for performing human identification. Classification and verification are performed using a MAP estimate and we show that we are able to achieve identification error rates as low as 2% and verification error rates as low as 0.3% on a database with 65 individuals with extreme illumination variations. The proposed method is easily able to deal with other distortions such as expressions and poses, and hence this establishes its robustness to intra-personal variations. A potential use of the method in illumination normalization is also discussed.


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  1. 1.
    Bensmail, H., Celeux, G., Raftery, A., Robert, C.P.: Inference in model-based cluster analysis. Statistics and Computing 7, 1–10 (1997)CrossRefGoogle Scholar
  2. 2.
    Gelfand, A.E., Hills, S.E., Racine-Poon, A., Smith, A.F.M.: Illustration of bayesian inference in normal data models using gibbs sampling. Journal of the American Statistical Association 85(412), 972–985 (1990)CrossRefGoogle Scholar
  3. 3.
    Gelman, A., Carlin, J.B., Stern, H.S., Rubin, D.B.: Bayesian Data Analysis. Chapman and Hall, Boca Raton (1995)Google Scholar
  4. 4.
    Havran, C., Hupet, L., Czyz, J., Lee, J., Vandendorpe, L., Verleysen, M.: Independent component analysis for face authentication. In: KES 2002 proceedings -Knowledge-Based Intelligent Information and Engineering Systems, Crema, Italy (2002)Google Scholar
  5. 5.
    Hayes, M.H.: The reconstruction of a multidimensional sequence from the phase or magnitude of its fourier transform. ASSP 30(2), 140–154 (1982)Google Scholar
  6. 6.
    Jonsson, K., Kittler, J., Li, Y.P., Matas, J.: Support vector machines for face authentication. In: Proceedings of BMVC 1999 (1999)Google Scholar
  7. 7.
    Li, Y., Kittler, J., Matas, J.: Effective implementation of Linear Discriminant Analysis for face recognition and verification. In: 8th International Conference on Computer Analysis and Patterns, Berlin (1999)Google Scholar
  8. 8.
    Liu, C., Zhu, S.C., Shum, H.Y.: Learning in homogeneous gibbs model of faces by minimax entropy. In: The IEEE International Conference on Computer Vision (ICCV), pp. 281–287 (2001)Google Scholar
  9. 9.
    McLachlan, G., Peel, D.: Finite Mixture Models. John Wiley and Sons, Chichester (2000)MATHCrossRefGoogle Scholar
  10. 10.
    Oppenheim, A.V., Schafer, R.W.: Discrete-time Signal Processing. Prentice Hall, NJ (1989)MATHGoogle Scholar
  11. 11.
    Palanivel, S., Venkatesh, B.S., Yegnanarayana, B.: Real time face authentication system using autoassociative neutral network models. In: Proceedings of IEEE International Conference on Multimedia and Expo, Baltimore (2003)Google Scholar
  12. 12.
    Savvides, M., Kumar, B.V.K.: Eigenphases vs Eigenfaces. In: ICPR (2004)Google Scholar
  13. 13.
    Savvides, M., Kumar, B.V.K., Khosla, P.K.: Corefaces - robust shift invariant PCA based correlation filter for illumination tolerant face recognition. In: CVPR (2004)Google Scholar
  14. 14.
    Savvides, M., Vijaya Kumar, B.V.K., Khosla, P.: Face verification using correlation filters. In: 3rd IEEE Automatic Identification Advanced Technologies, Tarrytown, NY, pp. 56–61 (2002)Google Scholar
  15. 15.
    Sim, T., Baker, S., Bsat, M.: The CMU pose, illumination, and expression (PIE) database. In: Proceedings of the 5th International Conference on Automatic Face and Gesture Recognition (2002)Google Scholar
  16. 16.
    Turk, M.A., Pentland, A.P.: Face recognition using Eigenfaces. In: Proceedings of CVPR (1991)Google Scholar
  17. 17.
    Voth, D.: In the news: face recognition technology. IEEE magazine on intelligent systems 18(3), 4–7 (2003)CrossRefGoogle Scholar
  18. 18.
    Yuille, A.: Deformable templates for face recognition. Journal of Cognitive Neuroscience 3(1) (1991)Google Scholar
  19. 19.
    Zhu, S., Wu, Y., Mumford, D.: Minimax entropy principle and its application to texture modeling. Neural Computation 9(8) (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Sinjini Mitra
    • 1
  • Marios Savvides
    • 2
  • Anthony Brockwell
    • 1
  1. 1.Department of StatisticsCarnegie Mellon UniversityPittsburghUSA
  2. 2.Electrical and Computer Engineering DepartmentCarnegie Mellon UniversityPittsburghUSA

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