Coupled Statistical Face Reconstruction

  • William A. P. Smith
  • Edwin R. Hancock
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3691)


We present a coupled statistical model that can be used to accurately recover facial surfaces from single images by jointly capturing variations in surface normal direction and surface height. The model is trained on range data. By fitting the model to surface normal data, the surface height function is implicitly recovered without having to integrate the recovered field of surface normals. We show how the coupled model can be fitted to image brightness data using geometric constraints on surface normal direction furnished by Lambert’s law.


Couple Model Active Appearance Model Depth Model Synthesise View Light Source Direction 
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.
    Atick, J.J., Griffin, P.A., Redlich, A.N.: Statistical approach to SFS: Reconstruction of 3D face surfaces from single 2D images. Neural Comp. 8, 1321–1340 (1996)CrossRefGoogle Scholar
  2. 2.
    Zhao, W.Y., Chellappa, R.: Illumination-insensitive face recognition using symmetric SFS. In: Proc. CVPR, pp. 286–293 (2000)Google Scholar
  3. 3.
    Prados, E., Faugeras, O.: Unifying approaches and removing unrealistic assumptions in shape from shading: Mathematics can help. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 141–154. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    Dovgard, R., Basri, R.: Statistical symmetric shape from shading for 3D structure recovery of faces. In: Proc. 8th European Conference on Computer Vision, vol. 2nd, pp. 99–113 (2004)Google Scholar
  5. 5.
    Nandy, D., Ben-Arie, J.: Shape from recognition: A novel approach for 3-D face shape recovery. IEEE Trans. Image Processing 10, 206–217 (2001)zbMATHCrossRefGoogle Scholar
  6. 6.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 484–498. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  7. 7.
    Sirovich, L.: Turbulence and the dynamics of coherent structures. Quart. Applied Mathematics XLV, 561–590 (1987)Google Scholar
  8. 8.
    Frankot, R.T., Chellappa, R.: A method for enforcing integrability in shape from shading algorithms. IEEE Trans. PAMI 10, 439–451 (1988)zbMATHGoogle Scholar
  9. 9.
    Georghiades, A., Belhumeur, P., Kriegman, D.: From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Trans. PAMI 23, 643–660 (2001)Google Scholar
  10. 10.
    Smith, W., Robles-Kelly, A., Hancock, E.R.: Reflectance correction for perspiring faces. In: Proc. ICIP, pp. 1389–1392 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • William A. P. Smith
    • 1
  • Edwin R. Hancock
    • 1
  1. 1.Department of Computer ScienceThe University of York 

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