Face to Face Communications in Multiplayer Online Games: A Real-Time System

  • Ce Zhan
  • Wanqing Li
  • Farzad Safaei
  • Philip Ogunbona
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4553)


Multiplayer online games (MOG) bring HCI into a new era of human-human interactions in computer world. Although current MOG provide more interactivity and social interaction in the virtual world, natural facial expression as a key factor in emulating face to face communications has been neglected by game designers. In this work, we propose a real-time automatic system to recognize players’ facial expressions, so that the recognition results can be used to drive the MOG’s “facial expression engine” instead of “text commands”. Our major contributions are the evaluation, improvement and efficient implementation of existing algorithms to build a real-time system that meets the requirements specifically imposed by MOGs. In particular, we use a smaller number of fixed facial landmarks based on our evaluation to reduce the computational load with little degradation of the recognition performance.


Facial Expression Recognition Rate Face Detection Facial Expression Recognition Gabor Wavelet 
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.


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  1. 1.
    RocResearch: Rocresearch 2004. Technical report, Video Game Industry, RocResearch ltd (2004)Google Scholar
  2. 2.
    Samal, A., Iyengar, P.A.: Automatic recognition and analysis of human faces and facial expressions: A survey. Pattern Recognition 25, 65–77 (1992)CrossRefGoogle Scholar
  3. 3.
    Fasel, B., Luttin, J.: Automatic facial expression analysis: Survey. Pattern Recognition 36, 259–275 (2003)zbMATHCrossRefGoogle Scholar
  4. 4.
    Pantic, M., Rothkrantz, L.: Automatic analysis of facial expressions: the state of the art. IEEE Transaction on Pattern Analysis and Machine Intelligence 22, 1424–1445 (2000)CrossRefGoogle Scholar
  5. 5.
    Tian, Y.L., Kanade, T., Cohn, J.F.: Hand book of face recognition. Springer, Heidelberg (2004)Google Scholar
  6. 6.
    Ekman, P.: Emotion in the Human Face. Cambridge University Press, Cambridge (1982)Google Scholar
  7. 7.
    Donato, G., Bartlett, M., Hager, J.C., Ekman, P., Sejnowski, T.J.: Classifying facial actions. IEEE Trans.Pattern Analysis and Machine Intelligence 21, 974–989 (1999)CrossRefGoogle Scholar
  8. 8.
    Kouzani, A.Z., He, F., Sammut, K.: Commonsense knowledge-based face detection. In: International Conference on Intelligent Engineering Systems (1997)Google Scholar
  9. 9.
    Kotropoulos, C., Pitas, I.: Rule-based face detection in frontal views.In: International Conference on Acoustics, Speech and Signal Processing. vol. 4, pp. 2537–2540 (1997)Google Scholar
  10. 10.
    Lu, X., Zheng, N., Zheng, S.: Linear sparse feature based face detection in gray images. In: International Conference on Image Processing (2003)Google Scholar
  11. 11.
    Han, C.C., Liao, Y.M., Yu, K.C., Chen, L.H.: Fast face detection via morphology-based pre-processing. In: 9th International Conference on Image Analysis and Processing, pp. 469–476 (1998)Google Scholar
  12. 12.
    Viola, Jones.: Robust real time object detection. In: Proceedings, 2nd International Workshop on Statistical and Computational Theories of Vision (2001)Google Scholar
  13. 13.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of online learning and an application to boosting. In: Vitányi, P.M.B. (ed.) EuroCOLT 1995. LNCS, vol. 904, pp. 23–37. Springer, Heidelberg (1995)Google Scholar
  14. 14.
    Antonin, G., Popovici, V., Thiran, J.P.: Independent component analysis and support vector machine for face feature extraction. In: 4th International Conference on Audio- and Video-Based Biometric Person Authentication, pp.111–118 (2003)Google Scholar
  15. 15.
    Ryu, Y.S., Oh, S.Y.: Automatic extraction of eye and mouth fields from a face image using eigenfeatures and ensemble networks. Applied Intelligence 17, 171–185 (2002)zbMATHCrossRefGoogle Scholar
  16. 16.
    Colbry, D., Stockman, G., Jain, A.K.: Detection of anchor points for 3d face verification. In: IEEE Workshop on Advanced 3D Imaging for Safety and Security (2005)Google Scholar
  17. 17.
    Xue, Z., Li, S.Z., Teoh, E.K.: Bayesian shape model for facial feature extraction and recognition. Pattern Recognition 36, 2819–2833 (2003)zbMATHCrossRefGoogle Scholar
  18. 18.
  19. 19.
    Ye, J., Zhan, Y., Song, S.: Facial expression features extraction based on gabor wavelet transformation. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 2215–2219 (2004)Google Scholar
  20. 20.
    Lyons, M.J., Akamatsu, S., Kamachi, M., Gyoba, J.: Coding facial expression with gabor wavelets. In: Proc. Third IEEE International Conference on Automatic Face and Gesture Recognition, pp. 200–205 (1998)Google Scholar
  21. 21.

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Ce Zhan
    • 1
  • Wanqing Li
    • 1
  • Farzad Safaei
    • 1
  • Philip Ogunbona
    • 1
  1. 1.University of Wollongong, Wollongong, NSW 2522Australia

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