A New Model and Process Architecture for Facial Expression Recognition

  • Ginés García-Mateos
  • Cristina Vicente-Chicote
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1876)


In this paper we address the problem of facial expression recognition. We have developed a new facial model based only on visual information. This model describes a set of bidimensional regions corresponding to those elements which most clearly define a facial expression. The problem of facial gestures classification has been divided into three subtasks: face segmentation, finding and describing relevant facial components and, finally, classifying them into one of the predefined categories. Each of these tasks can be solved independently using different techniques already applied to a wide range of problems. This have led us to the definition of a modular, generic and extensible process architecture. A prototype has been developed which makes use of different simple solutions for each module, using a controlled environment and a low-cost vision system. We report the experimental results achieved by the prototype on a set of test images.


Facial expression recognition facial modeling feature location facial segmentation facial components 


  1. 1.
    Pentland, A.: Looking at People: Sensing for Ubiquitous and Wearable Computing, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, (January 2000)Google Scholar
  2. 2.
    Ekman, P., Friesen, W.V.: Facial Action Coding System (FACS): A Technique for the Measurement of Facial Action. Manual, Consulting Psychologists Press, Palo Alto, CA (1978)Google Scholar
  3. 3.
    Rothkrantz, L.J.M., van Schouwen, M.R., Ververs, F., Vollering, J.C.M.: A multimedial tool for facial expressions, Euromadia’ 98, Leicester (1998)Google Scholar
  4. 4.
    Essa, I.A., Pentland, A.: Dynamic Facial Analysis System: Coding, Analysis, Interpretation, and Recognition of Facial Motions,, Massachusetts Institute of Technology (1996)
  5. 5.
    Pantic, M., Rothkrantz, L.J.M.: Automated Facial Expression Analysis, ASCI’98 Proceedings of the fourth annual conference of the Advanced School for Computing and Imaging, Lommel, Belgium (1998)Google Scholar
  6. 6.
    Kimura, S., Yachida, M.: Facial Expression Recognition and Its Degree Estimation, Proceedings IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR-97), Puerto Rico (1997)Google Scholar
  7. 7.
    Lanitis, A., et alt.: Automatic Interpretation and Coding of Face Images Using Flexible Models, IEEE Transactions on Pattern Analysis and Machine Intell., Vol. 19, No. 7, (July 1997)Google Scholar
  8. 8.
    Mase, K.: Recognition of facial expressions by optical flow, IEICE Transactions, Special Issue on Computer Vision and its Applications, E 74(10) (1991)Google Scholar
  9. 9.
    Yang, J., Waibel, A.: A Real-Time Face Tracker, Proceedings of WACV’96, Saratosa, Florida, pp. 142–147 (1996)Google Scholar
  10. 10.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active Contours Models, Proceedings First International Conference of Computer Vision, London, pp. 259–269 (1987)Google Scholar
  11. 11.
    Jain, A.K., Duin, R.P.W., Mao, J.: Statistical Pattern Recognition: A Review, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, (January 2000)Google Scholar
  12. 12.
    Lopez-de-Teruel, P.E., Ruiz, A.: On-Line Probabilistic Learning Techniques for Real Time Computer Vision, WorkShop Learning 98, Getafe, Spain (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Ginés García-Mateos
    • 1
  • Cristina Vicente-Chicote
    • 1
  1. 1.Dpto. de Lenguajes y Sistemas InformáticosUniversidad de MurciaMurciaSpain

Personalised recommendations