Discriminative Bayesian Active Shape Models

  • Pedro Martins
  • Rui Caseiro
  • João F. Henriques
  • Jorge Batista
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7574)


This work presents a simple and very efficient solution to align facial parts in unseen images. Our solution relies on a Point Distribution Model (PDM) face model and a set of discriminant local detectors, one for each facial landmark. The patch responses can be embedded into a Bayesian inference problem, where the posterior distribution of the global warp is inferred in a ımaximum a posteriori (MAP) sense. However, previous formulations do not model explicitly the covariance of the latent variables, which represents the confidence in the current solution. In our Discriminative Bayesian Active Shape Model (DBASM) formulation, the MAP global alignment is inferred by a Linear Dynamical System (LDS) that takes this information into account. The Bayesian paradigm provides an effective fitting strategy, since it combines in the same framework both the shape prior and multiple sets of patch alignment classifiers to further improve the accuracy. Extensive evaluations were performed on several datasets including the challenging Labeled Faces in the Wild (LFW). Face parts descriptors were also evaluated, including the recently proposed Minimum Output Sum of Squared Error (MOSSE) filter. The proposed Bayesian optimization strategy improves on the state-of-the-art while using the same local detectors. We also show that MOSSE filters further improve on these results.


Root Mean Square Linear Dynamical System Kernel Density Esti Active Appearance Model Bayesian Paradigm 
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.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE TPAMI 23, 681–685 (2001)CrossRefGoogle Scholar
  2. 2.
    Matthews, I., Baker, S.: Active appearance models revisited. IJCV 60, 135–164 (2004)CrossRefGoogle Scholar
  3. 3.
    Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical Report 07-49, University of Massachusetts, Amherst (2007)Google Scholar
  4. 4.
    Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models-their training and application. CVIU 61, 38–59 (1995)Google Scholar
  5. 5.
    Cristinacce, D., Cootes, T.F.: Boosted regression active shape models. In: BMVC (2007)Google Scholar
  6. 6.
    Tresadern, P., Bhaskar, H., Adeshina, S., Taylor, C., Cootes, T.F.: Combining local and global shape models for deformable object matching. In: BMVC (2009)Google Scholar
  7. 7.
    Cristinacce, D., Cootes, T.F.: Automatic feature localisation with constrained local models. Pattern Recognition 41, 3054–3067 (2008)zbMATHCrossRefGoogle Scholar
  8. 8.
    Wang, Y., Lucey, S., Cohn, J.: Enforcing convexity for improved alignment with constrained local models. In: IEEE CVPR (2008)Google Scholar
  9. 9.
    Gu, L., Kanade, T.: A Generative Shape Regularization Model for Robust Face Alignment. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 413–426. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Saragih, J., Lucey, S., Cohn, J.: Face alignment through subspace constrained mean-shifts. In: IEEE ICCV (2009)Google Scholar
  11. 11.
    Bolme, D.S., Beveridge, J.R., Draper, B.A., Lui, Y.M.: Visual object tracking using adaptive correlation filters. In: IEEE CVPR (2010)Google Scholar
  12. 12.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer (2006)Google Scholar
  13. 13.
    Paquet, U.: Convexity and bayesian constrained local models. In: IEEE CVPR (2009)Google Scholar
  14. 14.
    Shen, C., Brooks, M.J., Hengel, A.: Fast global kernel density mode seeking: Applications to localization and tracking. IEEE TIP 16, 1457–1469 (2007)Google Scholar
  15. 15.
    Nordstrom, M., Larsen, M., Sierakowski, J., Stegmann, M.: The IMM face database - an annotated dataset of 240 face images. Technical report, Technical University of Denmark, DTU (2004)Google Scholar
  16. 16.
    Jesorsky, O., Kirchberg, K.J., Frischholz, R.W.: Robust Face Detection Using the Hausdorff Distance. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 90–95. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  17. 17.
    Messer, K., Matas, J., Kittler, J., Luettin, J., Maitre, G.: XM2VTSDB: The extended M2VTS database. In: AVBPA (1999)Google Scholar
  18. 18.
    FGNet: Talking face video (2004)Google Scholar
  19. 19.
    Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., Lin, C.-J.: LIBLINEAR: A library for large linear classification. JMLR, 1871–1874 (2008)Google Scholar
  20. 20.
    Cristinacce, D., Cootes, T.F.: Facial feature detection using adaboost with shape constraints. In: BMVC (2003)Google Scholar
  21. 21.
    Cristinacce, D., Cootes, T.F.: Feature detection and tracking with constrained local models. In: BMVC (2006)Google Scholar
  22. 22.
    Viola, P., Jones, M.: Robust real-time object detection. IJCV 57, 137–154 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Pedro Martins
    • 1
  • Rui Caseiro
    • 1
  • João F. Henriques
    • 1
  • Jorge Batista
    • 1
  1. 1.Institute of Systems and RoboticsUniversity of CoimbraPortugal

Personalised recommendations