Facial Landmarks Detection Using Extended Profile LBP-Based Active Shape Models

  • Nelson Méndez
  • Leonardo Chang
  • Yenisel Plasencia-Calaña
  • Heydi Méndez-Vázquez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8259)


The accurate localization of facial features is an important task for the face recognition process. One of the most used approaches to achieve this goal is the Active Shape Models (ASM) method and its different extensions. In this work, a new method is proposed for obtaining a Local Binary Patterns (LBP) based profile for representing the local appearance of landmark points of the shape model in ASM. The experimental evaluation, conducted on XM2VTS and BioID databases, shows the good performance of the proposal.


facial landmarks facial features detection ASM LBP 


  1. 1.
    Cootes, T.F., 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
  2. 2.
    Zhou, D., Petrovska-Delacrétaz, D., Dorizzi, B.: Automatic landmark location with a Combined Active Shape Model. In: 3rd IEEE International Conference on Biometrics: Theory, Applications and Systems. BTAS 2009 (2009)Google Scholar
  3. 3.
    Cristinacce, D., Cootes, T.F.: Boosted Regression Active Shape Models. In: British Machine Vision Conference, pp. 79.1–79.10. BMVA Press (2007)Google Scholar
  4. 4.
    Huang, X., Li, S.Z., Wang, Y.: Shape localization based on statistical method using extended Local Binary Pattern. In: Third International Conference on Image and Graphics. ICIG 2004, IEEE Computer Society, USA (2004)Google Scholar
  5. 5.
    Marcel, S., Keomany, J., Rodriguez, Y.: Robust-to-illumination face localisation using Active Shape Models and Local Binary Patterns. Tech Report, Idiap-RR-47-2006, IDIAP (2006)Google Scholar
  6. 6.
    Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution Gray-scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)CrossRefGoogle Scholar
  7. 7.
    Rapp, V., Senechal, T., Bailly, K., Prevost, L.: Multiple kernel learning svm and statistical validation for facial landmark detection. In: FG, pp. 265–271. IEEE (2011)Google Scholar
  8. 8.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active Appearance Models. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(6), 681–685 (2001)CrossRefGoogle Scholar
  9. 9.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Comparing active shape models with active appearance models. In: British Machine Vision Conference (1999)Google Scholar
  10. 10.
    Cristinacce, D., Cootes, T.: Automatic feature localisation with constrained local models. Pattern Recogn. 41(10), 3054–3067 (2008)CrossRefzbMATHGoogle Scholar
  11. 11.
    Cristinacce, D., Cootes, T.F.: A comparison of shape constrained facial feature detectors. In: 6th International Conference on Automatic Face and Gesture Recognition 2004, Seoul, Korea, pp. 375–380 (2004)Google Scholar
  12. 12.
    Cristinacce, D., Cootes, T.F.: Facial feature detection and tracking with automatic template selection. In: FG, pp. 429–434. IEEE Computer Society (2006)Google Scholar
  13. 13.
    Messer, K., Matas, J., Kittler, J., Jonsson, K.: XM2VTSDB: The extended M2VTS database. In: Second International Conference on Audio and Video-based Biometric Person Authentication, pp. 72–77 (1999)Google Scholar
  14. 14.
    Sun, J., Wei, Y., Wen, F., Cao, X.: Face alignment by Explicit Shape Regression. In: IEEE Conf. on Computer Vision and Pattern Recognition, pp. 2887–2894 (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Nelson Méndez
    • 1
  • Leonardo Chang
    • 1
  • Yenisel Plasencia-Calaña
    • 1
  • Heydi Méndez-Vázquez
    • 1
  1. 1.Advanced Technologies Application CenterHavanaCuba

Personalised recommendations