Semi-Automatic Adaptation of High-Polygon Wireframe Face Models Through Inverse Perspective Projection

  • Kristin S. Benli
  • Didem Ag dog an
  • Mete Özgüz
  • M. Taner Eskil
Conference paper

Abstract

Precise registration of a generic 3D face model with a subject’s face is a critical stage for model based analysis of facial expressions. In this study we propose a semi-automatic model fitting algorithm to fit a high-polygon wireframe model to a single image of a face. We manually mark important landmark points both on the wireframe model and the face image. We carry out an initial alignment by translating and scaling the wireframe model. We then translate the landmark vertices in the 3D wireframe model so that they coincide with inverse perspective projections of image landmark points. The vertices that are not manually labeled as landmark are translated with a weighted sum of vectorial displacement of k neighboring landmark vertices, inversely weighted by their 3D distances to the vertex under consideration. Our experiments indicate that we can fit a high-polygon model to the subject’s face with modest computational complexity.

Keywords

Facial expression analysis Generic wireframe High-polygon Face model customization Adaptation Initialization Landmark Perspective projection 

Notes

Acknowledgments

This research is part of project "Expression Recognition based on Facial Anatomy", grant number 109E061, supported by The Support Programme for Scientific and Technological Research Projects (1001) of The Scientific and Technological Research Council of Turkey (TÜB?TAK).

References

  1. 1.
    Essa, I.A.: Coding, analysis, interpretation, and recognition of facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 19, 757–763 (1998)CrossRefGoogle Scholar
  2. 2.
    Moghaddam, B., Pentland, A.: Face recognition using view-based and modular eigenspaces. In: Automatic Systems for the Identification of Humans, vol. 2277. SPIE (1994)Google Scholar
  3. 3.
    Pentland, A., Moghaddam, B., Starner, T.: View-based and modular eigenspaces for face recognition. In: Computer vision and pattern recognition conference. IEEE Comput. Soc. pp. 84–91 (1994)Google Scholar
  4. 4.
    Cootes, T.F., Cooper, D., Taylor, C.J., Graham, J.: Active shape models—their training and application. In: Comput. Vis. Image Understand. 61(1), 38–59 (1995)Google Scholar
  5. 5.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: Burkhardt, H, Neumann, B. (eds.) Proceedings of the European Conference on Computer Vision 1998, vol. 2, pp. 484–498, Springer (1998)Google Scholar
  6. 6.
    Krinidis, S., Pitas, I.: Facial expression synthesis through facial expressions statistical analysis. In: European Signal Processing Conference (EUSIPCO06), Florence, Italy (2006)Google Scholar
  7. 7.

Copyright information

© Springer-Verlag London Limited  2011

Authors and Affiliations

  • Kristin S. Benli
    • 1
  • Didem Ag dog an
    • 1
  • Mete Özgüz
    • 1
  • M. Taner Eskil
    • 1
  1. 1.Department of Computer Science and Engineering, Pattern Recognition and Machine Intelligence LaboratoryISIK UniversityIstanbulTurkey

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