Task Specific Factors for Video Characterization

  • Ranjeeth Kumar
  • S. Manikandan
  • C. V. Jawahar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4338)


Factorization methods are used extensively in computer vision for a wide variety of tasks. Existing factorization techniques extract factors that meet requirements such as compact representation, interpretability, efficiency, dimensionality reduction etc. However, when the extracted factors lack interpretability and are large in number, identification of factors that cause the data to exhibit certain properties of interest is useful in solving a variety of problems. Identification of such factors or factor selection has interesting applications in data synthesis and recognition. In this paper simple and efficient methods are proposed, for identification of factors of interest from a pool of factors obtained by decomposing videos represented as tensors into their constituent low rank factors. The method is used to select factors that enable appearance based facial expression transfer and facial expression recognition. Experimental results demonstrate that the factor selection facilitates efficient solutions to these problems with promising results.


Facial Expression Singular Value Decomposition Basis Image Expression Recognition Facial Expression Recognition 
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.
    Tomasi, C., Kanade, T.: Shape and motion without depth. In: Proc. of the Third IEEE International Conf. on Computer Vision, pp. 91–95 (1990)Google Scholar
  2. 2.
    Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401, 788–791 (1999)CrossRefGoogle Scholar
  3. 3.
    Tenenbaum, J.B., Freeman, W.T.: Separating style and content with bilinear models. Neural Computation 12, 1247–1283 (2000)CrossRefGoogle Scholar
  4. 4.
    Wang, H., Ahuja, N.: Facial expression decomposition. In: Proc. of the Ninth IEEE International Conf. on Computer Vision, pp. 958–965 (2003)Google Scholar
  5. 5.
    Hazan, T., Polak, S., Shashua, A.: Sparse image coding using a 3d non-negative tensor factorization. Proc. of the Tenth IEEE International Conf. on Computer Vision, 50–57 (2005)Google Scholar
  6. 6.
    Ghahramani, Z., Hinton, G.E.: The EM algorithm for mixtures of factor analyzers. Technical Report CRG-TR-96-1 (1996)Google Scholar
  7. 7.
    Welling, M., Weber, M.: Positive tensor factorization. Pattern Recognition Letters 22, 1255–1261 (2001)MATHCrossRefGoogle Scholar
  8. 8.
    Alex, M., Vasilescu, O., Terzopoulos, D.: Multilinear analysis of image ensembles: Tensorfaces. In: Proc. of the Seventh European Conf. on Computer Vision, vol. 1, pp. 447–460 (2002)Google Scholar
  9. 9.
    Shashua, A., Levin, A.: Linear image coding for regression and classification using the tensor-rank principle. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 42–49 (2001)Google Scholar
  10. 10.
    Shashua, A., Hazan, T.: Non-negative tensor factorization with applications to statistics and computer vision. Proc. of the International Conf. on Machine Learning, 792–799 (2005)Google Scholar
  11. 11.
    Platt, J., Cristianini, N., Shawe-Taylor, J.: Large margin dags for multiclass classification. In: Solla, S.A., Leen, T.K., Mueller, K.-R. (eds.) Advances in Neural Information Processing Systems 12, pp. 547–553 (2000)Google Scholar
  12. 12.
    Sebe, N., Lew, M.S., Cohen, I., Garg, A., Huang, T.S.: Emotion recognition using a cauchy naive bayes classifier. In: Proc. of the 16 th International Conf. on Pattern Recognition, vol. 1, pp. 17–20 (2002)Google Scholar
  13. 13.
    Liu, Z., Shan, Y., Zhang, Z.: Expressive expression mapping with ratio images. In: Proc. of the 28th Annual Conf. on Computer Graphics and Interactive Techniques (SIGGRAPH), pp. 271–276 (2001)Google Scholar
  14. 14.
    Du, Y., Lin, X.: Mapping emotional status to facial expressions. In: Proc. of the International Conf. on Pattern Recognition, vol. II, pp. 524–527 (2002)Google Scholar
  15. 15.
    Ekman, P., Friesen, W.V.: Facial Action Coding System: Investigator’s Guide. Consulting Pshycologists Press, Palo Alto, CA, Palo Alto (1978)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ranjeeth Kumar
    • 1
  • S. Manikandan
    • 1
  • C. V. Jawahar
    • 1
  1. 1.Center for Visual Information TechnologyInternational Institute of Information TechnologyHyderabadIndia

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