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A Facial Expression Recognition Approach Based on Novel Support Vector Machine Tree

  • Qinzhen Xu
  • Pinzheng Zhang
  • Luxi Yang
  • Wenjiang Pei
  • Zhenya He
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4493)

Abstract

Automatic facial expression recognition is the kernel part of emotional information processing. This paper dedicates to develop an automatic facial expression recognition approach based on a novel support vector machine tree, which performs feature selection at each internal node, to improve recognition accuracy and robustness. After the Pseudo-Zernike moment features were extracted, they were used to train a support vector machine tree for automatic recognition. The structure of a support vector machine enables the model to divide the facial recognition problem into sub-problems according to the teacher signals, so that it can solve the sub-problems in decreased complexity in different tree levels. In the training phase, those sub-samples assigned to two internal sibling nodes perform decreasing confusion cross, thus, the generalization ability for recognition of facial expression is enhanced. The compared results on Cohn-Kanade facial expression database also show that the proposed approach appeared higher recognition accuracy and robustness than other approaches.

Keywords

Support Vector Machine Feature Selection Facial Expression Recognition Accuracy Internal Node 
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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References

  1. 1.
    Aleksic, P.S., Katsaggelos, A.K.: Automatic Facial Expression Recognition using Facial Animation Parameters and Multistream HMMs. IEEE Trans. Information Forensics and Security 1, 3–11 (2006)CrossRefGoogle Scholar
  2. 2.
    Ekman, P., Friesen, W.V.: Facial Action Coding System: A technique for the Measurement of Facial Movement. Consulting Psychologists Press, Palo Alto (1978)Google Scholar
  3. 3.
    Vapnik, V.N.: An Overview of Statistical Learning Theory. IEEE Trans. Neural Networks 10, 988–999 (1999)CrossRefGoogle Scholar
  4. 4.
    Xu, Q.Z., Song, A.G., Pei, W.J., Yang, L.X., He, Z.Y.: Tree-Structured Support Vector Machine with Confusion Cross for Complex Pattern Recognition Problems. In: Proceedings of 2005 IEEE International Workshop on VLSI Design and Video Technology, pp. 195–198 (2005)Google Scholar
  5. 5.
    Pang, S.N., Kim, D.J., Bang, S.Y.: Face Membership Authentication using SVM Classification Tree Generated by Membership based LLE Data Partition. IEEE Trans. Neural networks 16, 436–446 (2005)CrossRefGoogle Scholar
  6. 6.
    Fasel, B., Luettin, J.: Automatic Facial Expression Analysis: A Survey. Pattern Recognition 36, 259–275 (2003)zbMATHCrossRefGoogle Scholar
  7. 7.
    Pardás, M., Bonafonte, A.: Facial Animation Parameters Extraction and Expression Detection using HMM. Signal Processing: Image Communication 17, 675–688 (2002)CrossRefGoogle Scholar
  8. 8.
    Teh, C.H., Chin, R.T.: On Image Analysis by the Methods of Moments. IEEE Trans. Pattern Analysis and Machine Intelligence 10, 496–513 (1988)zbMATHCrossRefGoogle Scholar
  9. 9.
    Chong, C.W., Raveendran, P., Mukundan, R.: An Efficient Algorithm for Fast Computation of Peseudo-Zernike Moments. Int. J. Pattern Recognition and Artificial Intelligence 17, 1011–1023 (2003)CrossRefGoogle Scholar
  10. 10.
    Foody, G.M., Mathur, A.: A Relative Evaluation of Multiclass Image Classification by Vector Machines. IEEE Trans. Geoscience and Remote Sensing 42, 1335–1343 (2004)CrossRefGoogle Scholar
  11. 11.
    Xu, Q.Z., Pei, W.J., Yang, L.X., He, Z.Y.: Support Vector Machine Tree Based on Feature Selection. In: King, I., Wang, J., Chan, L.-W., Wang, D. (eds.) ICONIP 2006. LNCS, vol. 4232, pp. 856–863. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  12. 12.
    Sindhwani, V., Rakshit, S., Deodhare, D., Erdogmus, D., Principe, J.C., Nivogi, P.: Feature Selection in MLPs and SVMs based on Maximum Output Information. IEEE Trans. Neural Networks 15, 937–948 (2004)CrossRefGoogle Scholar
  13. 13.
    Kanade, T., Cohn, J.F., Tian, Y.I.: Comprehensive Database for Facial Expression Analysis. In: Proc. of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 46–53 (2000)Google Scholar
  14. 14.
    Hsu, C.W., Lin, C.J.: A Comparison of Methods for Multiclass Support Vector Machines. IEEE Trans. Neural Networks 13, 415–525 (2002)CrossRefGoogle Scholar
  15. 15.
    Littlewort, G., Bartlett, M., Fasel, I., Susskind, J., Movellan, J.R.: Dynamics of Facial Expression Extracted Automatically from Eideo. In: Proc. Of IEEE Conference on Computer Vision and Pattern Recognition, Workshop on Face Processing in Video, Orlando, FL, vol. 5, p. 80 (2004)Google Scholar
  16. 16.
    Chibelushi, C.C., Bourel, F.: Hierarchical Multistream Recognition of Facial Expressions. IEE Proceedings on Vision, Image and Signal Processing 151, 307–313 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Qinzhen Xu
    • 1
  • Pinzheng Zhang
    • 2
  • Luxi Yang
    • 1
  • Wenjiang Pei
    • 1
  • Zhenya He
    • 1
  1. 1.School of Information Science and Engineering, Southeast University, Nanjing, 210096China
  2. 2.School of Computer Science and Engineering, Southeast University, Nanjing, 210096China

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