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)


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.


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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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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