Advertisement

Facial Expression Recognition for Domestic Service Robots

  • Geovanny Giorgana
  • Paul G. Ploeger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7416)

Abstract

We present a system to automatically recognize facial expressions from static images. Our approach consists of extracting particular Gabor features from normalized face images and mapping them into three of the six basic emotions: joy, surprise and sadness, plus neutrality. Selection of the Gabor features is performed via the AdaBoost algorithm. We evaluated two learning machines (AdaBoost and Support Vector Machines), two multi-classification strategies (Error-Correcting Output Codes and One-vs-One) and two face image sizes (48 x 48 and 96 x 96). Images of the Cohn-Kanade AU-Coded Facial Expression Database were used as test bed for our research. Best results (87.14% recognition rate) were obtained using Support Vector Machines in combination with Error-Correcting Output Codes and normalized face images of 96 x 96.

Keywords

Facial expression recognition Gabor features AdaBoost Support Vector Machines Error-correcting output codes One-vs-One multi-classification Face normalization 

References

  1. 1.
    Bartlett, M.S., Littlewort, G., Frank, M., Lainscsek, C., Fasel, I., Movellan, J.: Recognizing Facial Expression: Machine Learning and Application to Spontaneous Behavior. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 568–573 (2005)Google Scholar
  2. 2.
    Deng, H.B., Jin, L.W., Zhen, L.X., Huang, J.C.: A New Facial Expression Recognition Method Based on Local Gabor Filter Bank and PCA plus LDA. International Journal of Information Technology 11, 86–96 (2005)Google Scholar
  3. 3.
    Dietterich, T.G., Bakiri, G.: Solving Multiclass Learning Problems via Error-Correcting Output Codes. Journal of Artificial Intelligence Research 2, 263–286 (1995)Google Scholar
  4. 4.
    Freund, Y., Schapire, R.E.: A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. In: Proceedings of the Second European Conference on Computational Learning Theory, pp. 23–37 (1995)Google Scholar
  5. 5.
    Kanade, T., Cohn, J.F., Tian, Y.: Comprehensive Database for Facial Expression Analysis. In: Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition (FG 2000), pp. 46–53 (2000)Google Scholar
  6. 6.
    Koutlas, A., Fotiadis, D.I.: An Automatic Region Based Methodology for Facial Expression Recognition. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 662–666 (2008)Google Scholar
  7. 7.
    Kulikowski, J.J., Marčelja, S., Bishop, P.O.: Theory of Spatial Position and Spatial Frequency Relations in the Receptive Fields of Simple Cells in the Visual Cortex. Biological Cybernetics 43, 187–198 (1982)Google Scholar
  8. 8.
    Shen, L., Bai, L.: Adaboost Gabor Feature Selection for Classification. In: Proc. of Image and Vision Computing, Akaroa, New Zealand, pp. 77–83 (2004)Google Scholar
  9. 9.
    Shih, F.Y., Chuang, C.F.: Automatic Extraction of Head and Face Boundaries and Facial Features. Information Sciences 158, 117–130 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Geovanny Giorgana
    • 1
  • Paul G. Ploeger
    • 1
  1. 1.Bonn-Rhein-Sieg University of Applied SciencesSankt AugustinGermany

Personalised recommendations