Facial Expression Recognition Using 3D Facial Feature Distances

  • Hamit Soyel
  • Hasan Demirel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4633)


In this paper, we propose a novel approach for facial expression analysis and recognition. The proposed approach relies on the distance vectors retrieved from 3D distribution of facial feature points to classify universal facial expressions. Neural network architecture is employed as a classifier to recognize the facial expressions from a distance vector obtained from 3D facial feature locations. Facial expressions such as anger, sadness, surprise, joy, disgust, fear and neutral are successfully recognized with an average recognition rate of 91.3%. The highest recognition rate reaches to 98.3% in the recognition of surprise.


3D Facial Expression Analysis Facial Feature Points Neural Networks 


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  1. 1.
    Ekman, P., Friesen, W.: The facial action coding system: a technique for the measurement of facial movement. Consulting Psychologists Press, San Francisco (1978)Google Scholar
  2. 2.
    Ekman, P., Huang, T.S., Sejnowski, T.J., Hager, J.C. (eds.): Final Report to NSF of the Planning Workshop on Facial Expression Understanding. Human Interaction Lab. Univ. California, San Francisco (1993)Google Scholar
  3. 3.
    Bartlett, M., Littlewort, G., Frank, M., Lainscsek, C., Fasel, I., Movellan, J.: Recognizing facial expressions: machine learning and application to spontaneous behavior. In: IEEE CVPR 2005, San Diego, CA, vol. 2, pp. 568–573 (2005)Google Scholar
  4. 4.
    Braathen, B., Bartlett, M., Littlewort, G., Smith, E., Movellan, J.: An approach to automatic recognition of spontaneous facial actions. In: Proc. of Int. Conf. on FGR, USA, pp. 345–350 (2002)Google Scholar
  5. 5.
    Cohen, I., Sebe, N., Garg, A., Chen, L.S., Huang, T.S.: Facial expression recognition from video sequences: temporal and static modeling. In: CVIU, vol. 91, pp. 160–187 (2003)Google Scholar
  6. 6.
    Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., Taylor, J.: Emotion recognition in human computer interaction. IEEE Signal Processing Magazine 18(1), 32–80 (2001)CrossRefGoogle Scholar
  7. 7.
    Pantic, M., Rothkrantz, L.: Facial action recognition for facial expression analysis from static face images. IEEE Trans. on SMC-Part B: Cybernetics 34, 1449–1461 (2004)CrossRefGoogle Scholar
  8. 8.
    Pantic, M., Rothkrantz, L.J.M.: Automatic analysis of facial expressions: The state of the art. IEEE Trans. on PAMI 22(12), 1424–1445 (2000)Google Scholar
  9. 9.
    Fasel, B., Luettin, J.: Automatic facial expression analysis: A survey. Pattern Recognition 36, 259–275 (2003)zbMATHCrossRefGoogle Scholar
  10. 10.
    Pandzic, I., Forchheimer, R.: MPEG-4 Facial Animation: the Standard, Implementation and Applications. Wiley, Chichester (2002)Google Scholar
  11. 11.
    Yin, L., Wei, X., Sun, Y., Wang, J., Rosato, M.: A 3d facial expression database for facial behavior research. In: Proc. of Int. Conf. on FGR, UK, pp. 211–216 (2006)Google Scholar
  12. 12.
    Parke, F., Waters, K.: Computer Facial Animation (1996)Google Scholar
  13. 13.
    Wang, J., Yin, L., Wei, X., Sun, Y.: 3D Facial Expression Recognition Based on Primitive Surface Feature Distribution. In: IEEE CVPR 2006, vol. 2, pp. 1399–1406 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Hamit Soyel
    • 1
  • Hasan Demirel
    • 1
  1. 1.Department of Electrical and Electronic Engineering, Eastern Mediterranean University, Gazimağusa, KKTC, via Mersin 10Turkey

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