Keypoint Identification and Feature-Based 3D Face Recognition

  • Ajmal Mian
  • Mohammed Bennamoun
  • Robyn Owens
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)


We present a feature-based 3D face recognition algorithm and propose a keypoint identification technique which is repeatable and identifies keypoints where shape variation is high in 3D faces. Moreover, a unique 3D coordinate basis can be defined locally at each keypoint facilitating the extraction of highly descriptive pose invariant features. A feature is extracted by fitting a surface to the neighbourhood of a keypoint and sampling it on a uniform grid. Features from a probe and gallery face are projected to the PCA subspace and matched. Two graphs are constructed from the set of matching features of the probe and gallery face. The similarity between these graphs is used to determine the identity of the probe. The proposed algorithm was tested on the FRGC v2 data and achieved 93.5% identification and 97.4% verifiction rates.


Face Recognition Range Image Neutral Expression Face Recognition Grand Challenge Gallery Face 
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 2007

Authors and Affiliations

  • Ajmal Mian
    • 1
  • Mohammed Bennamoun
    • 1
  • Robyn Owens
    • 1
  1. 1.School of Computer Science and Software Engineering, The University of Western Australia, 35 Stirling Highway, Crawley, WA 6009Australia

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