Facial Shape Spaces from Surface Normals

  • Simone Ceolin
  • William A. P. Smith
  • Edwin Hancock
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5112)


In this paper, we draw on ideas from the field of statistical shape analysis to construct shape-spaces that span facial expressions and gender, and use the resulting shape-model to perform face recognition under varying expression and gender. Our novel contribution is to show how to construct shape-spaces over fields of surface normals rather than Cartesian landmark points. According to this model face needle-maps (or fields of surface normals) are points in a high-dimensional manifold referred to as a shape-space. We compute geodesic distances to compare the similarity between faces and gender difference.


Shape-Space Surface Normals Geodesic distance 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Simone Ceolin
    • 1
  • William A. P. Smith
    • 1
  • Edwin Hancock
    • 1
  1. 1.Computer Vision and Pattern Recognition Group,Computer ScienceUniversity of York HeslingtonYorkUnited Kingdom

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