2007 | OriginalPaper | Buchkapitel
Learning Mixture Models for Gender Classification Based on Facial Surface Normals
verfasst von : Jing Wu, W. A. P. Smith, E. R. Hancock
Erschienen in: Pattern Recognition and Image Analysis
Verlag: Springer Berlin Heidelberg
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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.