Learning Mixture Models for Gender Classification Based on Facial Surface Normals

  • Jing Wu
  • W. A. P. Smith
  • E. R. Hancock
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4477)

Abstract

The aim in this paper is to show how to discriminate gender using a parameterized representation of fields of facial surface normals (needle-maps). We make use of principle geodesic analysis (PGA) to parameterize the facial needle-maps. Using feature selection, we determine the selected feature set which gives the best result in distinguishing gender. Using the EM algorithm we distinguish gender by fitting a two component mixture model to the vectors of selected features. Results on real-world data reveal that the method gives accurate gender discrimination results.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Jing Wu
    • 1
  • W. A. P. Smith
    • 1
  • E. R. Hancock
    • 1
  1. 1.Department of Computer Science, The University of York, York, YO10 5DDUK

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