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Person Authentication from Video of Faces: A Behavioral and Physiological Approach Using Pseudo Hierarchical Hidden Markov Models

  • Manuele Bicego
  • Enrico Grosso
  • Massimo Tistarelli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)

Abstract

In this paper a novel approach to identity verification, based on the analysis of face video streams, is proposed, which makes use of both physiological and behavioral features. While physical features are obtained from the subject’s face appearance, behavioral features are obtained by asking the subject to vocalize a given sentence. The recorded video sequence is modelled using a Pseudo-Hierarchical Hidden Markov Model, a new type of HMM in which the emission probability of each state is represented by another HMM. The number of states are automatically determined from the data by unsupervised clustering of expressions of faces in the video. Preliminary results on real image data show the feasibility of the proposed approach.

Keywords

Facial Expression Hide Markov Model Face Recognition Video Sequence Emission Probability 
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.

References

  1. 1.
    Bicego, M., Castellani, U., Murino, V.: Using Hidden Markov Models and wavelets for face recognition. In: IEEE. Proc. of Int. Conf on Image Analysis and Processing, pp. 52–56 (2003)Google Scholar
  2. 2.
    Fine, S., Singer, Y., Tishby, N.: The hierarchical hidden markov model: Analysis and applications. Machine Learning 32, 41–62 (1998)MATHCrossRefGoogle Scholar
  3. 3.
    Hadid, A., Pietikäinen, M.: An experimental investigation about the integration of facial dynamics in video-based face recognition. Electronic Letters on Computer Vision and Image Analysis 5(1), 1–13 (2005)Google Scholar
  4. 4.
    Jain, A.K., Dubes, R.: Algorithms for clustering data. Prentice-Hall, Englewood Cliffs (1988)MATHGoogle Scholar
  5. 5.
    Knight, B., Johnston, A.: The role of movement in face recognition. Visual Cognition 4, 265–274 (1997)CrossRefGoogle Scholar
  6. 6.
    Lee, K.C., Ho, J., Yang, M.H., Kriegman, D.: Video-based face recognition using probabilistic appearance manifolds. In: Proc. Int. Conf. on Computer Vision and Pattern Recognition (2003)Google Scholar
  7. 7.
    Li, C.: A Bayesian Approach to Temporal Data Clustering using Hidden Markov Model Methodology. PhD thesis, Vanderbilt University (2000)Google Scholar
  8. 8.
    Liu, X., Chen, T.: Video-based face recognition using adaptive hidden markov models. In: Proc. Int. Conf. on Computer Vision and Pattern Recognition (2003)Google Scholar
  9. 9.
    Nefian, A.V., Hayes, M.H.: Hidden Markov models for face recognition. In: Proc. Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Seattle, pp. 2721–2724 (1998)Google Scholar
  10. 10.
    O‘Toole, A.J., Roark, D.A., Abdi, H.: Recognizing moving faces: A psychological and neural synthesis. Trends in Cognitive Science 6, 261–266 (2002)CrossRefGoogle Scholar
  11. 11.
    Panuccio, A., Bicego, M., Murino, V.: A Hidden Markov model-based approach to sequential data clustering. In: Caelli, T.M., Amin, A., Duin, R.P.W., Kamel, M.S., de Ridder, D. (eds.) SPR 2002 and SSPR 2002. LNCS, vol. 2396, pp. 734–742. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  12. 12.
    Rabiner, L.: A tutorial on Hidden Markov Models and selected applications in speech recognition. Proc. of IEEE 77(2), 257–286 (1989)CrossRefGoogle Scholar
  13. 13.
    Samaria, F.: Face recognition using Hidden Markov Models. PhD thesis, Engineering Department, Cambridge University (October 1994)Google Scholar
  14. 14.
    Schwarz, G.: Estimating the dimension of a model. The Annals of Statistics 6(2), 461–464 (1978)MATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: A literature survey. ACM Computing Surveys 35, 399–458 (2003)CrossRefGoogle Scholar
  16. 16.
    Zhou, S., Krueger, V., Chellappa, R.: Probabilistic recognition of human faces from video. Computer Vision and Image Understanding 91, 214–245 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Manuele Bicego
    • 1
  • Enrico Grosso
    • 1
  • Massimo Tistarelli
    • 2
  1. 1.DEIRUniversity of SassariSassariItaly
  2. 2.DAPUniversity of SassariAlghero (SS)Italy

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