Learning 3D AAM Fitting with Kernel Methods

  • Marina A. Cidota
  • Dragos Datcu
  • Leon J. M. Rothkrantz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7267)


The active appearance model (AAM) has proven to be a powerful tool for modeling deformable visual objects. AAMs are nonlinear parametric models in terms of the relation between the pixel intensities and the parameters of the model. In this paper, we propose a fitting procedure for a 3D AAM based on kernel methods for regression. The use of kernel functions provides a powerful way of detecting nonlinear relations using linear algorithms in an appropriate feature space. For analysis, we have chosen the relevance vector machines (RVM) and the kernel ridge method. The statistics computed on data generated with our 3D AAM implementation show that the kernel methods give better results compared to the linear regression models. Although they are less computational efficient, due to their higher accuracy the kernel methods have the advantage of reducing the searching space for the 3D AAM fitting algorithm.


3D-Active Appearance Models nonlinear optimization kernel methods 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bishop, C.: Pattern Recognition and Machine Learning, 1st edn. Springer Science+Business Media, New York (2006)zbMATHGoogle Scholar
  2. 2.
    Bradski, G., Kaehler, A.: Learning OpenCV, 1st edn. Reilly Media, Inc., Sebastopol (2008)Google Scholar
  3. 3.
    Cootes, T., Edwards, G., Taylor, C.: Active Appearance Models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998, Part II. LNCS, vol. 1407, pp. 484–498. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  4. 4.
    Cootes, T., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models: their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)CrossRefGoogle Scholar
  5. 5.
    Datcu, D., Rothkrantz, L.: The use of active appearance model for facial expression recognition in crisis environments. In: Proceedings of ISCRAM, pp. 515–524 (2007)Google Scholar
  6. 6.
    Datcu, D., Rothkrantz, L.: Semantic audio-visual data fusion for automatic emotion recognition. In: Euromedia 2008, Porto, Eurosis, Ghent, pp. 58–65 (April 2008) ISBN: 978-9077381-38-0Google Scholar
  7. 7.
    Dornaika, F., Ahlberg, J.: Fitting 3D face models for tracking and active appearance model training. Image and Vision Computing 24(9), 1010–1024 (2006)CrossRefGoogle Scholar
  8. 8.
    Edwards, G., Taylor, C., Cootes, T.: Face Recognition Using Active Appearance Models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998, Part II. LNCS, vol. 1407, pp. 581–595. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  9. 9.
    Edwards, G., Taylor, C., Cootes, T.: Interpreting face images using active appearance models. In: FG 1998: Proceedings of the 3rd International Conference on Face & Gesture Recognition. IEEE Computer Society, Washington, DC (1998)Google Scholar
  10. 10.
    Lefèvre, S., Odobez, J.M.: View-Based Appearance Model Online Learning for 3D Deformable Face Tracking. In: Proc. Int. Conf. on Computer Vision Theory and Applications (VISAPP), Angers (May 2010)Google Scholar
  11. 11.
    Marcel, S., Keomany, J., Rodriguez, Y.: Robust to illumination face localization using shape models and local binary patterns. Technical report, IDIAP (2006)Google Scholar
  12. 12.
    Martinez, W., Martinez, A.: Computational Statistics HandBook with MATLAB. Chapman&Hall/CRC, New York (2002)Google Scholar
  13. 13.
    Matthews, I., Baker, S.: Active appearance models revisited. International Journal of Computer Vision, 135–164 (2004)Google Scholar
  14. 14.
    Saragih, J., Gocke, R.: Learning aam fitting through simulation. Pattern Recognition (42), 2628–2636 (2009)Google Scholar
  15. 15.
    Savran, A., Alyüz, N., Dibeklioğlu, H., Çeliktutan, O., Gökberk, B., Sankur, B., Akarun, L.: Bosphorus Database for 3D Face Analysis. In: Schouten, B., Juul, N.C., Drygajlo, A., Tistarelli, M. (eds.) BIOID 2008. LNCS, vol. 5372, pp. 47–56. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  16. 16.
    Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, New York (2004)CrossRefGoogle Scholar
  17. 17.
    Stegmann, M.: The AAM-API: An Open Source Active Appearance Model Implementation. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2879, pp. 951–952. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  18. 18.
    Tipping, M.: Sparse bayesian learning and the relevance vector machine. The Journal of Machine Learning Research 1 (2001)Google Scholar
  19. 19.
    Tong, Y., Liao, W., Ji, Q.: Facial action unit recognition by exploiting their dynamic and semantic relationships. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(10), 1683–1699 (2007)CrossRefGoogle Scholar
  20. 20.
    Viola, P., Jones, M.: Robust real-time face detection. International Journal of Computer Vision 57(2), 137–154 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Marina A. Cidota
    • 1
  • Dragos Datcu
    • 2
  • Leon J. M. Rothkrantz
    • 2
  1. 1.Faculty of Mathematics and Computer ScienceUniversity of BucharestBucharestRomania
  2. 2.Netherlands Defense AcademyDen HelderThe Netherlands

Personalised recommendations