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Learning mixture models for gender classification based on facial surface normals

Wu, Jing, Smith, W. A. P. and Hancock, E. R. 2007. Learning mixture models for gender classification based on facial surface normals. Presented at: Third Iberian Conference, IbPRIA 2007, Girona, Spain, 6-8 June 2007. Published in: Marti, J., Benedí, J. M., Mendonça, A. M. and Serrat, J. eds. Pattern Recognition and Image Analysis: Third Iberian Conference, IbPRIA 2007, Girona, Spain, June 6-8, 2007, Proceedings, Part I. Lecture Notes in Computer Science , vol. 4477. Springer Berlin Heidelberg, pp. 39-46. 10.1007/978-3-540-72847-4_7

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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.

Item Type: Conference or Workshop Item (Paper)
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > Q Science (General)
T Technology > T Technology (General)
Publisher: Springer Berlin Heidelberg
ISBN: 9783540728467
ISSN: 03029743
Last Modified: 04 Jun 2017 09:03
URI: http://orca.cf.ac.uk/id/eprint/89873

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