Advertisement

Semi-supervised Learning of Caricature Pattern from Manifold Regularization

  • Junfa Liu
  • Yiqiang Chen
  • Jinjing Xie
  • Xingyu Gao
  • Wen Gao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5371)

Abstract

Automatic caricature synthesis is to transform the input face to an exaggerated one. It is becoming an interesting research topic, but it remains an open issue to specify the caricature’s pattern for the input face. This paper proposed a novel pattern prediction method based on MR (manifold regularization), which comprises three steps. Firstly, we learn the caricature pattern by manifold dimension reduction, and select some low dimensional caricature pattern as the labels for corresponsive true faces. Secondly, manifold regularization is performed to build a semi-supervised regression between true faces and the pattern labels. In the third step of offline phase, the input face is mapped to a pattern label by the learnt regressive model, and the pattern label is further transformed to caricature parameters by a locally linear reconstruction algorithm. This approach takes advantage of manifold structure lying in both true faces and caricatures. Experiments show that, low dimensional manifold represents the caricature pattern well and the semi-supervised regressive model from manifold regularization can predict the target caricature pattern successfully.

Keywords

Pattern Label Unlabeled Sample Mesh Parameter Input Face Facial Mesh 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Brennan, S.: Caricature generator. Massachusettes Institute of Technology, Cambridge (1982)Google Scholar
  2. 2.
    Koshimizu, H., Tominaga, M., Fujiwara, T., et al.: On Kansei facial processing for computerized facial caricaturing system Picasso. In: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, Tokyo, pp. 294–299 (1999)Google Scholar
  3. 3.
    Akleman, E.: Making caricature with morphing. In: Visual Proceedings of ACM SIGGRAPH 1997, p. 145 (1997)Google Scholar
  4. 4.
    Chiang, P.-Y., Liao, W.-H., Li, T.-Y.: Automatic Caricature Generation by Analyzing Facial Features. In: 2004 Asian Conference on Computer Vision, Jeju Island, Korea, January 27-30 (2004)Google Scholar
  5. 5.
    Gooch, B., Reinhard, E., Gooch, A.: Human facial illustrations: Creation and psychophysical evaluation. ACM Trans. Graph. 23(1), 27–44 (2004)CrossRefGoogle Scholar
  6. 6.
    Chen, H., Xu, Y., Shum, H., Zhu, S., Zheng, N.: Example based facial sketch generation with non-parametric sampling. In: ICCV 2001, pp. II: 433–II: 438 (2001)Google Scholar
  7. 7.
    Liang, L., Chen, H., Xu, Y.-Q., Shum, H.-Y.: Example-based Caricature Generation with Exaggeration. In: IEEE Proceedings of the 10th Pacific Conference on Computer Graphics and Applications (2002)Google Scholar
  8. 8.
    Liu, J., Chen, Y., Gao, W.: Mapping Learning in Eigenspace for Harmonious Caricature Generation. In: 14th ACM International Conference on Multimedia, Santa Barbara, USA, October 22-27, 2006, pp. 683–686 (2006)Google Scholar
  9. 9.
    Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000)CrossRefGoogle Scholar
  10. 10.
    Belkin, M., Niyogi, P., Sindhwani, V.: Manifold Regularization: a Geometric Framework for Learning from Labeled and Unlabeled Examples. Journal of Machine Learning Research 7, 2399–2434 (2006)MathSciNetzbMATHGoogle Scholar
  11. 11.
    Evgeniou, T., Pontil, M., Poggio, T.: Regularization Networks and Support Vector Machines. In: Advances in Computational Mathematics, vol. 13, pp. 1–50 (2000)Google Scholar
  12. 12.
    Wolberg, G.: Digital Image Warping. IEEE Computer Society Press, Los Alamitos (1990)Google Scholar
  13. 13.
    Cootes, T.F., Taylor, C.J., Cooper, D., Graham, J.: Active shape models–their training and application. Computer vision and image understanding 61(1), 38–59 (1995)CrossRefGoogle Scholar
  14. 14.
    Blanz, V., Vetter, T.: A morphable model for the synthesis of 3D-faces. In: Proc. SIGGRAPH 1999, pp. 187–194 (1999)Google Scholar
  15. 15.
    Shet, R.N., Lai, K.H., Edirisinghe, E.A., Chung, P.W.H.: Use of Neural Networks in Automatic Caricature Generation: An Approach Based on Drawing Style Capture. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds.) IbPRIA 2005. LNCS, vol. 3523, pp. 343–351. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  16. 16.
    Sousa, M.C., Samavati, F., Brunn, M.: Depicting Shape Features with Directional Strokes and Spotlighting. In: IEEE Proceedings of the Computer Graphics International (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Junfa Liu
    • 1
    • 2
  • Yiqiang Chen
    • 1
  • Jinjing Xie
    • 1
    • 2
  • Xingyu Gao
    • 1
  • Wen Gao
    • 1
    • 3
  1. 1.Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  2. 2.Graduate School of Chinese Academy of SciencesBeijingChina
  3. 3.Institute of Digital MediaPeking UniversityBeijingChina

Personalised recommendations