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Face View Synthesis Across Large Angles

  • Jiang Ni
  • Henry Schneiderman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3723)

Abstract

Pose variations, especially large out-of-plane rotations, make face recognition a difficult problem. In this paper, we propose an algorithm that uses a single input image to accurately synthesize an image of the person in a different pose. We represent the two poses by stacking their information (pixels or feature locations) in a combined feature space. A given test vector will consist of a known part corresponding to the input image and a missing part corresponding to the synthesized image. We then solve for the missing part by maximizing the test vector’s probability. This approach combines the “distance-from-feature-space” and “distance-in-feature-space”, and maximizes the test vector’s probability by minimizing a weighted sum of these two distances. Our approach does not require either 3D training data or a 3D model, and does not require correspondence between different poses. The algorithm is computationally efficient, and only takes 4 – 5 seconds to generate a face. Experimental results show that our approach produces more accurate results than the commonly used linear-object-class approach. Such technique can help face recognition to overcome the pose variation problem.

Keywords

Ground Truth Face Recognition Face Image Frontal View Synthetic Image 
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.
    Black, M., Jepson, A.: Eigen-tracking: Robust matching and tracking of articulated objects using a view-based representation. International Journal of Computer Vision 36(2), 101–130 (1998)Google Scholar
  2. 2.
    Blanz, V., Vetter, T.: A Morphable Model for the Synthesis of 3D Faces. ACM Siggraph (1999)Google Scholar
  3. 3.
    Blanz, V., Vetter, T.: Face Recognition Based on Fitting a 3D Morphable Model. IEEE TPAMI 25(9), 1063–1074 (2003)Google Scholar
  4. 4.
    Blanz, V., Grother, P., Phillips, P.J., Vetter, T.: Face Recognition Based on Frontal Views generated from Non-Frontal Images. In: CVPR (2005)Google Scholar
  5. 5.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active Appearance Models. IEEE TPAMI 23(6), 681–685 (2001)Google Scholar
  6. 6.
    Debevec, P., Taylor, C., Malik, J.: Modeling and rendering architecture from photographs: A hybrid geometry- and image-based approach. In: Computer Graphics (SIGGRAPH), pp. 11–20 (1996)Google Scholar
  7. 7.
    Gross, R., Matthews, I., Baker, S.: Appearance-Based Face Recognition and Light-Fields. IEEE TPAMI 26(4), 449–465 (2004)Google Scholar
  8. 8.
    Hwang, B.-W., Lee, S.-W.: Reconstruction of Partially Damaged Face Images Based on a Morphable Face Model. IEEE TPAMI 25(3), 365–372 (2003)Google Scholar
  9. 9.
    Leonardis, A., Bischof, H.: Robust recognition using eigenimages. Computer Vision and Image Understanding 78(1), 99–118 (2000)CrossRefGoogle Scholar
  10. 10.
    Moghaddam, B., Pentland, A.: Probabilistic Visual Learning for Object Representation. IEEE TPAMI 19(7), 696–710 (1997)Google Scholar
  11. 11.
    Sim, T., Baker, S., Bsat, M.: The CMU Pose, Illumination, and Expression Database. IEEE TPAMI 25(12), 1615–1618 (2003)Google Scholar
  12. 12.
    Vetter, T., Poggio, T.: linear object classes and Image Synthesis From a Single Example Image. IEEE TPAMI 19(7), 733–742 (1997)Google Scholar
  13. 13.
    Xiao, J.: Reconstruction, Registration, and Modeling of Deformable Object Shapes. CMU RI PhD thesis (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jiang Ni
    • 1
  • Henry Schneiderman
    • 1
  1. 1.Robotics InstituteCarnegie Mellon UniversityPittsburghUSA

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