Gender classification using principal geodesic analysis and gaussian mixture models

Authors:
Jing Wu;William A. P. Smith;Edwin R. Hancock
Affiliations:
Department of Computer Science, The University of York, York, UK;Department of Computer Science, The University of York, York, UK;Department of Computer Science, The University of York, York, UK
Venue:
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Year:
2006

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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) which can be extracted from 2D intensity images using shape-from-shading (SFS). We makes use of principle geodesic analysis (PGA) to parameterize the facial needle-maps. Using feature selection, we determine which of the components of the resulting parameter vector are the most significant 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 gender discrimination results that are comparable to human observers.