Semi-supervised Facial Expressions Annotation Using Co-Training with Fast Probabilistic Tri-Class SVMs

  • Mohamed Farouk Abdel Hady
  • Martin Schels
  • Friedhelm Schwenker
  • Günther Palm
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6353)

Abstract

Supervised learning requires a large amount of labeled data but the data labeling process can be expensive and time consuming, as it requires the efforts of human experts. Semi-supervised learning methods that can reduce the amount of required labeled data through exploiting the available unlabeled data to improve the classification accuracy. Here, we propose a learning framework to exploit the unlabeled data by decomposing multi-class problems into a set of binary problems and apply Co-Training to each binary problem. A probabilistic version of Tri-Class Support Vector Machine is proposed (SVM) that can discriminate between ignorance and uncertainty and an updated version of Sequential Minimal Optimization (SMO) algorithm is used for fast learning of Tri-Class SVMs. The proposed framework is applied to facial expressions recognition task. The results show that Co-Training can exploit effectively the independent views and the unlabeled data to improve the recognition accuracy of facial expressions.

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References

  1. 1.
    Kanade, T., Cohn, J., Tian, Y.L.: Comprehensive database for facial expression analysis. In: Proc. of the 4th IEEE Int. Conf. on Automatic Face and Gesture Recognition, pp. 46–53 (2000)Google Scholar
  2. 2.
    Angulo, C., Ruiz, F.J., González, L., Ortega, J.A.: Multi-classification by using Tri-Class SVM. Neural Processing Letters 23(1), 89–101 (2006)CrossRefGoogle Scholar
  3. 3.
    Shashua, A., Levin, A.: Taxonomy of large margin principle algorithms for ordinal regression problems. In: NIPS, vol. 15. MIT Press, Cambridge (2002)Google Scholar
  4. 4.
    Chu, W., Keerthi, S.S.: New approaches to support vector ordinal regression. In: Proc. of the 22nd Int. Conf. on Machine Learning, pp. 145–152 (2005)Google Scholar
  5. 5.
    Blum, A., Mitchell, T.: Combining labeled and unlabeled data with Co-Training. In: Proc. of the 11th Annual Conference on Computational Learning Theory, pp. 92–100. Morgan Kaufmann, San Francisco (1998)Google Scholar
  6. 6.
    Bayerl, P., Neumann, H.: Disambiguating visual motion through contextual feedback modulation. Neural Comput. 16(10), 2041–2066 (2004)MATHCrossRefGoogle Scholar
  7. 7.
    Campbell, W.M., Sturim, D.E., Reynolds, D.A.: Support vector machines using GMM supervectors for speaker verification. IEEE Signal Processing Letters 13(5), 308–311 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Mohamed Farouk Abdel Hady
    • 1
  • Martin Schels
    • 1
  • Friedhelm Schwenker
    • 1
  • Günther Palm
    • 1
  1. 1.Institute of Neural Information ProcessingUniversity of UlmUlmGermany

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