Pattern Recognition and Image Analysis

, Volume 18, Issue 3, pp 447–452 | Cite as

Facial expression recognition based on Haar-like feature detection

  • A. Panning
  • A. K. Al-Hamadi
  • R. Niese
  • B. Michaelis
Application Problems


In this paper we propose a novel approach for facial feature detection in color image sequences using Haar-like classifiers. The feature extraction is initialized without manual input and has the capability to fulfill the real time requirement. For facial expression recognition, we use geometrical measurement and simple texture analysis in detecting facial regions based on the prior detected facial feature points. For expression classification we used a three layer feed forward artificial neural network. The efficiency of the suggested approach is demonstrated under real world conditions.


Facial Expression Facial Feature Facial Expression Recognition Facial Action Code System Facial Feature Point 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    P. Ekman and W. V. Friesen, “Facial Action Coding System (FACS),” Consulting Psychologies Press, 1978.Google Scholar
  2. 2.
    G. L. Ford, et al., “Fully Automatic Coding of Basic Expressions from Video,” Tech. Report INC-MPLab-TR-2002.03, Machine Perception Lab, Institute for Neural Computation, University of California.Google Scholar
  3. 3.
    J. Skelley, R. Fischer, A. Sarma, and B. Heisele, “Recognizing Expressions in a New Database Containing Played and Natural Expressions,” ICPR, 2006, vol. 1, pp. 1220–1225.Google Scholar
  4. 4.
    S. Ioannou, M. Wallace, and S. Kollias, “Intelligent Facial Analysis and Expression Recognition,” Proceedings of International Joint Conference on Neural Networks (IJCNN) 2006 (Vancouver, Canada, 2006).Google Scholar
  5. 5.
    N. Sebe, I. Cohen, T. Gevers, and T. S. Huang, “Emotion Recognition Based on Joint Visual and Audio Cues,” ICPR, 2006, Vol. 1, pp. 1136–1139.Google Scholar
  6. 6.
    K. Anderson and P. McOwan, “A Real-Time Automated System for the Recognition of Human Facial Expressions,” IEEE Transactions on Systems, Man, and Cybernetics—Part B, 2006 (in press).Google Scholar
  7. 7.
    C.-S. Lee and A. Elgammal, “Nonlinear Shape and Appearance Models for Facial Expression Analysis and Synthesis,” ICPR 1, 497–502 (2006).Google Scholar
  8. 8.
    B. Abboud, F. Davoine, and M. Dang, “Statistical Modeling for Facial Expression Analysis and Synthesis,” ICIP 2003 (IEEE, Barcelona, 2003), Vol. 1.Google Scholar
  9. 9.
    P. Viola and M. Jones, “Rapid Object Detection Using a Boosted Cascade of Simple Features,” Proceedings of IEEE Conf. on Computer Vision and Pattern Recognition, 2001.Google Scholar
  10. 10.
    R. Lienhart and J. Maydt, “An Extended Set of Haar-Like Features for Rapid Object Detection,” IEEE ICIP 1, 900–903 (2002).Google Scholar
  11. 11.
    Open Source Computer Vision Library, “openCV”,
  12. 12.
    P. I. Wilson and J. Fernandez, “Facial Feature Detection Using Haar Classifiers,” The Journal of Computing Sciences in Colleges 21(4), (2006).Google Scholar
  13. 13.
    T. Wang and P. Shi, “Facial Components Detection with Boosting and Geometric Constraints”, ICPR 1, 446–449 (2006).MathSciNetGoogle Scholar
  14. 14.
    F. Dornaika and F. Davoine, “Facial Expression Recognition Using Auto-Regressive Models,” ICPR 2, 520–523 (2006).Google Scholar
  15. 15.
    F. Wallhoff, “Facial Expressions and Emotion Database,” (Technische Universität MÜnchen, 2006).
  16. 16.
    S. Nissen and E. Nemerson, “Fast Artifical Neural Network,”

Copyright information

© Pleiades Publishing, Ltd. 2008

Authors and Affiliations

  • A. Panning
    • 1
  • A. K. Al-Hamadi
    • 1
  • R. Niese
    • 1
  • B. Michaelis
    • 1
  1. 1.Institute for Electronics, Signal Processing and CommunicationsOtto-von-Guericke-University MagdeburgMagdeburgGermany

Personalised recommendations