Learning Mixture Models for Gender Classification Based on Facial Surface Normals

Authors:
Jing Wu;W. A. Smith;E. R. Hancock
Affiliations:
Department of Computer Science, The University of York, York, YO10 5DD, UK;Department of Computer Science, The University of York, York, YO10 5DD, UK;Department of Computer Science, The University of York, York, YO10 5DD, UK
Venue:
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Year:
2007

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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.