Advertisement

Exploring the Identity Manifold: Constrained Operations in Face Space

  • Ankur Patel
  • William A. P. Smith
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6316)

Abstract

In this paper, we constrain faces to points on a manifold within the parameter space of a linear statistical model. The manifold is the subspace of faces which have maximally likely distinctiveness and different points correspond to unique identities. We show how the tools of differential geometry can be used to replace linear operations such as warping and averaging with operations on the surface of this manifold. We use the manifold to develop a new method for fitting a statistical face shape model to data, which is both robust (avoids overfitting) and overcomes model dominance (is not susceptible to local minima close to the mean face). Our method outperforms a generic non-linear optimiser when fitting a dense 3D morphable face model to data.

Keywords

Vector Length Angular Error Principal Component Analysis Model Target Face Active Appearance Model 
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.
    Amberg, B., Blake, A., Fitzgibbon, A., Romdhani, S., Vetter, T.: Reconstructing high quality face-surfaces using model based stereo. In: Proc. ICCV (2007)Google Scholar
  2. 2.
    Blanz, V., Scherbaum, K., Seidel, H.P.: Fitting a morphable model to 3D scans of faces. In: Proc. ICCV (2007)Google Scholar
  3. 3.
    Blanz, V., Vetter, T.: Face recognition based on fitting a 3D morphable model. IEEE Trans. Pattern Anal. Mach. Intell. 25(9), 1063–1074 (2003)CrossRefGoogle Scholar
  4. 4.
    Broyden, C.G.: The convergence of a class of double-rank minimization algorithms. Journal of the Institute of Mathematics and Its Applications 6(1), 76–90 (1970)MATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    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
  6. 6.
    Hu, C., Xiao, J., Matthews, I., Baker, S., Cohn, J., Kanade, T.: Fitting a single active appearance model simultaneously to multiple images. In: Proc. BMVC (2004)Google Scholar
  7. 7.
    Kiefer, J.: Sequential minimax search for a maximum. Proceedings of the American Mathematical Society 4(3), 502–506 (1953)MATHMathSciNetGoogle Scholar
  8. 8.
    Knothe, R., Romdhani, S., Vetter, T.: Combining PCA and LFA for surface reconstruction from a sparse set of control points. In: Proc. Int. Conf. on Automatic Face and Gesture Recognition, pp. 637–644 (2006)Google Scholar
  9. 9.
    Matthews, I., Baker, S.: Active appearance models revisited. Int. J. Comput. Vis. 60(2), 135–164 (2004)CrossRefGoogle Scholar
  10. 10.
    Meytlis, M., Sirovich, L.: On the dimensionality of face space. IEEE Trans. Pattern Anal. Mach. Intell. 29(7), 1262–1267 (2007)CrossRefGoogle Scholar
  11. 11.
    O’Toole, A.J., Vetter, T., Volz, H., Salter, E.M.: Three-dimensional caricatures of human heads: Distinctiveness and the perception of facial age. Perception 26(6), 719–732 (1997)CrossRefGoogle Scholar
  12. 12.
    Paysan, P., Knothe, R., Amberg, B., Romdhani, S., Vetter, T.: A 3D face model for pose and illumination invariant face recognition. In: Proc. IEEE Intl. Conf. on Advanced Video and Signal based Surveillance (2009)Google Scholar
  13. 13.
    Pennec, X.: Probabilities and statistics on Riemannian manifolds: basic tools for geometric measurements. In: Proc. IEEE Workshop on Nonlinear Signal and Image Processing (1999)Google Scholar
  14. 14.
    Romdhani, S., Vetter, T.: Estimating 3D shape and texture using pixel intensity, edges, specular highlights, texture constraints and a prior. In: Proc. CVPR, vol. 2, pp. 986–993 (2005)Google Scholar
  15. 15.
    Sarkar, S.: USF humanid 3D face database (2005)Google Scholar
  16. 16.
    Valentine, T.: A unified account of the effects of distinctiveness, inversion, and race in face recognition. Quarterly Journal of Experimental Psychology A 43(2), 161–204 (1991)Google Scholar
  17. 17.
    Xiao, J., Baker, S., Matthews, I., Kanade, T.: Real–time combined 2D+3D active appearance models. In: Proc. CVPR, pp. 535–542 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ankur Patel
    • 1
  • William A. P. Smith
    • 1
  1. 1.Department of Computer ScienceThe University of York 

Personalised recommendations