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Parametric Stereo for Multi-pose Face Recognition and 3D-Face Modeling

  • Rik Fransens
  • Christoph Strecha
  • Luc Van Gool
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3723)

Abstract

This paper presents a new method for face modeling and face recognition from a pair of calibrated stereo cameras. In a first step, the algorithm builds a stereo reconstruction of the face by adjusting the global transformation parameters and the shape parameters of a 3D morphable face model. The adjustment of the parameters is such that stereo correspondence between both images is established, i.e. such that the 3D-vertices of the model project on similarly colored pixels in both images. In a second step, the texture information is extracted from the image pair and represented in the texture space of the morphable face model. The resulting shape and texture coefficients form a person specific feature vector and face recognition is performed by comparing query vectors with stored vectors. To validate our algorithm, an extensive image database was built. It consists of stereo-pairs of 70 subjects. For recognition testing, the subjects were recorded under 6 different head directions, ranging from a frontal to a profile view. The face recognition results are very good, with 100% recognition on frontal views and 97% recognition on half-profile views.

Keywords

Face Recognition Recognition Rate Query Vector Texture Computation Stereo Correspondence 
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.
    Bartlett, M.S., Lades, H.M., Sejnowski, T.J.: Independent component representations for face recognition. Proc. of the SPIE Symposium on Electonic Imaging: Science and Technology, pp. 528–539 (1998)Google Scholar
  2. 2.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans. PAMI 19(7), 711–720 (1997)Google Scholar
  3. 3.
    Belhumeur, P., Kriegman, D.: What is the Set of Images of an Object Under All Possible Lighting Conditions? IJCV 28(3), 245–260 (1998)CrossRefGoogle Scholar
  4. 4.
    Beymer, D.: Face recognition under varying pose. Tech. Rep. 1461. MIT AI Lab, Massachusetts Institute of Technology, Cambridge, MAGoogle Scholar
  5. 5.
    Beymer, D.: Vectorizing face images by interleaving shape and texture computations. Tech. Rep. 1537, MIT AI Lab, Massachusetts Institute of Technology, Cambridge, MAGoogle Scholar
  6. 6.
    Blanz, V., Vetter, T.: A morphable model for the synthesis of 3D faces. In: SIGGRAPH 1999: Proc. of the 26th Annual Conference on Computer Graphics and Interactive Techniques, pp. 187–194 (1999)Google Scholar
  7. 7.
    Blanz, V., Vetter, T.: Face Recognition Based on Fitting a 3D Morphable Model. IEEE Trans. PAMI 25(9), 1063–1074 (2003)Google Scholar
  8. 8.
    Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active Shape Models - Their Training and Application. Computer Vision and Image Understanding 61(1), 38–59 (1995)CrossRefGoogle Scholar
  9. 9.
    Dimitrijevic, M., Ilic, S., Fua, P.: Accurate Face Models from Uncalibrated and Ill-Lit Video Sequences. In: IEEE Proc. Int. Conf. CVPR, vol. 2, pp. 1034–1041 (2004)Google Scholar
  10. 10.
    Lanitis, A., Taylor, C.J., Cootes, T.F.: Automatic Face Identification System Using Flexible Appearance Models. Image Vis. Comput. 13, 393–401 (1995)CrossRefGoogle Scholar
  11. 11.
    Moghaddam, B., Pentland, A.: Probabilistic Visual Learning for Object Representation. IEEE Trans. Pattern Anal. Mach. Intell. 19, 696–710 (1997)CrossRefGoogle Scholar
  12. 12.
    Pentland, A., Moghaddam, B., Starner, T.: View-Based and Modular Eigenspaces for Face Recognition. In: Proc. Int. Conf. Computer Vision and Pattern Recognition, pp. 84–91 (1994)Google Scholar
  13. 13.
    Shan, Y., Liu, Z., Zhang, Z.: Model-Based Bundle Adjustment with Application to Face Modeling. In: International Conference on Computer Vision, vol. 2, p. 644 (2001)Google Scholar
  14. 14.
    Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience 3(1) (1991)Google Scholar
  15. 15.
    Wiskott, L., Fellous, J.-M., Kruger, N., von der Malsburg, C.: Face Recognition by Elastic Bunch Graph Matching. IEEE Trans. PAMI 19(7), 775–779 (1997)Google Scholar
  16. 16.
    Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: A literature survey. ACM Comput. Surv. 35(4), 399–458 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Rik Fransens
    • 1
  • Christoph Strecha
    • 1
  • Luc Van Gool
    • 1
  1. 1.PSI ESAT-KULLeuvenBelgium

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