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Automatic Facial Expression Recognition with AAM-Based Feature Extraction and SVM Classifier

  • Xiaoyi Feng
  • Baohua Lv
  • Zhen Li
  • Jiling Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4293)

Abstract

In this paper, an effective method is proposed for automatic facial expression recognition from static images. First, a modified Active Appearance Model (AAM) is used to locate facial feature points automatically. Then, based on this, facial feature vector is formed. Finally, SVM classifier with a sample selection method is adopted for expression classification. Experimental results on the JAFFE database demonstrate an average recognition rate of 69.9% for novel expressers, showing that the proposed method is promising.

Keywords

Facial Expression Local Binary Pattern Expression Recognition Facial Expression Recognition Active Appearance Model 
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.
    Pantic, M., Rothkrantz, L.J.M.: Automatic analysis of facial expressions: the state of the art. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 1424–1445 (2000)CrossRefGoogle Scholar
  2. 2.
    Fasel, B., Luettin, J.: Automatic facial expression analysis: A survey. Pattern Recognition 36, 259–275 (2003)MATHCrossRefGoogle Scholar
  3. 3.
    Fellenz, W., Taylor, J., Tsapatsoulis, N., Kollias, S.: Comparing template-based, feature-based and supervised classification of facial expression from static images. Computational Intelligence and Applications (1999)Google Scholar
  4. 4.
    Lyons, M., Budynek, J., Akamastu, S.: Automatic classification of single facial images. IEEE Trans. Pattern Analysis and Machine Intelligence 21, 1357–1362 (1999)CrossRefGoogle Scholar
  5. 5.
    Zhang, Z.: Feature-based facial expression recognition: Sensitivity analysis and experiment with a multi-layer perceptron. Pattern Recognition and Artificial Intelligence 13, 893–911 (1999)CrossRefGoogle Scholar
  6. 6.
    Zheng, W., Zhou, X., Zou, C., Zhao, L.: Facial expression recognition using kernel canonical correlation analysis (KCCA). IEEE Trans. Neural Networks 17, 233–238 (2006)CrossRefGoogle Scholar
  7. 7.
    Shinohara, Y., Otsu, N.: Facial Expression Recognition Using Fisher Weight Maps. In:IEEE Conf. on Automatic Face and Guesture Recognition, pp. 499–504 (2004)Google Scholar
  8. 8.
    Feng, X., Hadid, A., Pietikainen, M.: A Coarse-to-Fine Classification Scheme for Facial Expression Recognition, Image Analysis and Recognition. In: Campilho, A.C., Kamel, M. (eds.) ICIAR 2004. LNCS, vol. 3212, pp. 668–675. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Feng, X., Hadid, A., Pietikainen, M.: Facial Expression Recognition with Local Binary Patterns and Linear Programming. Pattern Recognition and Image Analysis 15, 546–549 (2005)Google Scholar
  10. 10.
    Cootes, T.F., Kittipanya-ngam, P.: Comparing variations on the active appearance model algorithm. BMVC, 837–846 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xiaoyi Feng
    • 1
  • Baohua Lv
    • 1
  • Zhen Li
    • 1
  • Jiling Zhang
    • 1
  1. 1.School of Electronic and InformationNorthwestern Polytechnic UniversityXi’anChina

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