English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Conference Paper

3D-reconstruction of faces: Combining stereo with class-based knowledge

MPS-Authors
/persons/resource/persons84298

Wallraven,  C
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons83815

Blanz,  V
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84280

Vetter,  T
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Wallraven, C., Blanz, V., & Vetter, T. (1999). 3D-reconstruction of faces: Combining stereo with class-based knowledge. In W. Förstner, J. Buhmann, A. Faber, & P. Faber (Eds.), Mustererkennung 1999: 21. DAGM-Symposium Bonn, 15.–17. September 1999 (pp. 405-412). Berlin, Germany: Springer.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-E65B-9
Abstract
The recovery of the threedimensional structure of faces with conventional stereo methods still proves difficult. In this paper we introduce a higher order constraint based on linear object classes, which supplies a standard stereo algorithm with prior knowledge of the general structure of faces. This constraint has been learned by exploiting the similarities between 200 faces in a database and is represented in a morphable face model. This combined approach has been tested and compared against an already existing method for estimating depth information using only prior knowledge and against the standard stereo algorithm.