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Video-Based Facial Expression Hallucination: A Two- Level Hierarchical Fusion Approach

  • Jian Zhang
  • Yueting Zhuang
  • Fei Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4179)

Abstract

Facial expression hallucination is an important approach to facial expression synthesis. Existing works mainly focused on synthesizing a static facial expression image given one face image with neutral expression. In this paper, we propose a novel two-level hierarchical fusion approach to hallucinate dynamic expression video sequences when given only one neutral expression face image. By fusion of local linear and global nonlinear subspace learning, the two-level approach provides a sound solution to organizing the complex video sample space. Experiments show that our approach generates reasonable facial expression sequences both in temporal domain and spatial domain with less artifact compared with existing works.

Keywords

Facial Expression Face Image Neighborhood Size Subspace Learning Video Sample 
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.
    Qingshan, Z., Zicheng, L., Baining, G., Harry, S.: Geometry-Driven Photorealistic Facial Expression Synthesis. In: Eurographics/SIGGRAPH Symposium on Computer Animation, San Diego, CA, pp. 177–186 (2003)Google Scholar
  2. 2.
    Zicheng, L., Ying, S., Zhengyou, Z.: Expressive Expression Mapping with Ratio Images. In: Proceedings of ACM SIGGRAPH. Los Angeles, California, pp. 271–276 (2001) Google Scholar
  3. 3.
    Simon, B., Takeo, K.: Hallucinating Faces. In: Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition, Grenoble, France, pp. 83–88 (2000)Google Scholar
  4. 4.
    Ce, L., Heung-Yeung, S., Chang-Shui, Z.: A Two-step Approach to Hallucinating Faces: Global Parametric Model and Local Nonparametric Model. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Kauai Marriott, Hawaii, pp. 192–198 (2001)Google Scholar
  5. 5.
    Yang, L., Xueyin, L.: An Improved Two-step Approach to Hallucinating Faces. In: Proceedings of The Third International Conference on Image and Graphics, Hong Kong (2004)Google Scholar
  6. 6.
    Wei, L., Dahua, L., Xiaoou, T.: Face Hallucination Through Dual Associative Learning. In: IEEE International Conference on Image Processing, vol. 1, pp. 11–14, I-873-6 (2005)Google Scholar
  7. 7.
    Wei, L., Dahua, L., Xiaoou, T.: Hallucinating Faces: TensorPatch Super-Resolution and Coupled Residue Compensation. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 478–484 (2005)Google Scholar
  8. 8.
    Wei, L., Dahua, L., Xiaoou, T.: Neighbor Combination and Transformation for Hallucinating Faces. In: Proceedings IEEE International Conference on Multimedia and Expo, pp. 145–148 (2005)Google Scholar
  9. 9.
    Xiaogang, W., Xiaoou, T.: Hallucinating Face by Eigentransformation. IEEE Transactions on Systems, Man, and Cybernetics—PART C: Applications and Reviews 35, 425–434 (2005)CrossRefGoogle Scholar
  10. 10.
    Congyong, S., Li, H.: Facial Expression Hallucination. In: Seventh IEEE Workshops on Application of Computer Vision, vol. 1, pp. 93–98 (2005)Google Scholar
  11. 11.
    Matthew, T., Alex, P.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience 3, 71–86 (1991)CrossRefGoogle Scholar
  12. 12.
    Sam, T.R., Lawrence, K.S.: Nonlinear Dimensionality Reduction by Locally Linear Embedding. SCIENCE 290(5500), 2323–2326 (2000)CrossRefGoogle Scholar
  13. 13.
    Jaco, V., Simon, J.G., Arnaud, D.: Radial Basis Function Regression Using Trans-dimensional Sequential Monte Carlo. In: IEEE Workshop on Statistical Signal Processing (2003)Google Scholar
  14. 14.
    PJonathon, P., Hyeonjoon, M., Patrick, R., Syed, A.R: The FERET Evaluation Methodology for Face Recognition Algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10) (2000) Google Scholar
  15. 15.
    Martinez, A., Benavente, R.: The AR Face Database. CVC Technical Report #24 (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jian Zhang
    • 1
  • Yueting Zhuang
    • 1
  • Fei Wu
    • 1
  1. 1.College of Computer Science & TechnologyZhejiang UniversityHangzhouP.R. China

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