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Automatic 3D Face Recognition Using Discriminant Common Vectors

  • Cheng Zhong
  • Tieniu Tan
  • Chenghua Xu
  • Jiangwei Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)

Abstract

In this paper we propose a fully automatic scheme for 3D face recognition. In our scheme, the original 3D data is automatically converted into the normalized 3D data, then the discriminant common vector (DCV) is introduced for 3D face recognition. We also compare DCV with two common methods, i.e., principal component analysis (PCA) and linear discriminant analysis (LDA). Our experiments are based on the CASIA 3D Face Database, a challenging database with complex variations. The experimental results show that DCV is superior to the other two methods.

Keywords

Face Recognition Linear Discriminant Analysis Recognition Rate Near Neighbor Original Vector 
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

  • Cheng Zhong
    • 1
  • Tieniu Tan
    • 1
  • Chenghua Xu
    • 1
  • Jiangwei Li
    • 1
  1. 1.National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingP.R. China

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