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)


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.


Feature Selection Mixture Model Gaussian Mixture Model Gender Discrimination Sequential Forward Selection 
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 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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