Locating Facial Features with an Extended Active Shape Model

  • Stephen Milborrow
  • Fred Nicolls
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5305)


We make some simple extensions to the Active Shape Model of Cootes et al. [4], and use it to locate features in frontal views of upright faces. We show on independent test data that with the extensions the Active Shape Model compares favorably with more sophisticated methods. The extensions are (i) fitting more landmarks than are actually needed (ii) selectively using two- instead of one-dimensional landmark templates (iii) adding noise to the training set (iv) relaxing the shape model where advantageous (v) trimming covariance matrices by setting most entries to zero, and (vi) stacking two Active Shape Models in series.


Facial Feature Mahalanobis Distance Shape Model Kernel Principal Component Analysis Active Appearance Model 
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.
    Bates, D., Maechler, M.: Matrix: A Matrix package for R. See the nearPD function in this R package for methods of forcing positive definiteness (2008),
  2. 2.
    Breiman, Friedman, Olshen, Stone: Classification and Regression Trees. Wadsworth (1984)Google Scholar
  3. 3.
    Cootes, T.F., Cooper, D.H., Taylor, C.J., Graham, J.: A Trainable Method of Parametric Shape Description. BMVC 2, 54–61 (1991)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.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active Appearance Models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 484–498. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  6. 6.
    Cootes, T.F., Taylor, C.J.: A Mixture Model for Representing Shape Variation. Image and Vision Computing 17(8), 567–574 (1999)CrossRefGoogle Scholar
  7. 7.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Comparing Active Shape Models with Active Appearance Models. In: Pridmore, T., Elliman, D. (eds.) Proc. British Machine Vision Conference, vol. 1, pp. 173–182 (1999)Google Scholar
  8. 8.
    Cootes, T.F., Taylor, C.J.: Technical Report: Statistical Models of Appearance for Computer Vision. The University of Manchester School of Medicine (2004),
  9. 9.
    Cootes, T.F., et al.: FGNET manual annotation of face datasets (2002),
  10. 10.
    Cristinacce, D., Cootes, T.: Feature Detection and Tracking with Constrained Local Models. BMVC 17, 929–938 (2006)Google Scholar
  11. 11.
    Gentle, J.E.: Numerical Linear Algebra for Applications in Statistics. Springer, Heidelberg (1998); See page 178 for methods of forcing positive definitenesszbMATHGoogle Scholar
  12. 12.
    van Ginneken, B., Frangi, A.F., Stall, J.J., ter Haar Romeny, B.: Active Shape Model Segmentation with Optimal Features. IEEE-TMI 21, 924–933 (2002)Google Scholar
  13. 13.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, Heidelberg (2003); See chapter 7 for methods of model assessmentGoogle Scholar
  14. 14.
    Intel: Open Source Computer Vision Library. Intel (2007)Google Scholar
  15. 15.
    Jesorsky, O., Kirchberg, K., Frischholz, R.: Robust Face Detection using the Hausdorff Distance. AVBPA 90–95 (2001)Google Scholar
  16. 16.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active Contour Models. IJCV 1, 321–331 (1987)CrossRefGoogle Scholar
  17. 17.
    Koenderink, J.J., van Doorn, A.J.: The Structure of Locally Orderless Images. IJCV 31(2/3), 159–168 (1999)CrossRefGoogle Scholar
  18. 18.
    Martinez, A.M., Benavente, R.: The AR Face Database: CVC Tech. Report 24 (1998)Google Scholar
  19. 19.
    Messer, K., Matas, J., Kittler, J., Luettin, J., Maitre, G.: XM2VTS: The Extended M2VTS Database. AVBPA (1999)Google Scholar
  20. 20.
    Milborrow, S.: Stasm software library (2007),
  21. 21.
    Rogers, M., Graham, J.: Robust Active Shape Model Search. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 517–530. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  22. 22.
    Romdhani, S., Gong, S., Psarrou, A.: A Multi-view Non-linear Active Shape Model using Kernel PCA. BMVC 10, 483–492 (1999)Google Scholar
  23. 23.
    Scholkopf, S., Smola, A., Muller, K.: Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Computation 10(5), 1299–1319 (1998)CrossRefGoogle Scholar
  24. 24.
    Li, Y., Ito, W.: Shape Parameter Optimization for AdaBoosted Active Shape Model. ICCV 1, 251–258 (2005)Google Scholar
  25. 25.
    Zhou, Y., Gu, L., Zhang, H.J.: Bayesian Tangent Shape Model: Estimating Shape and Pose Parameters via Bayesian Inference. In: CVPR (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Stephen Milborrow
    • 1
  • Fred Nicolls
    • 1
  1. 1.Department of Electrical EngineeringUniversity of Cape TownSouth Africa

Personalised recommendations