Learning mixture models for gender classification based on facial surface normals

Wu, Jing ORCID: https://orcid.org/0000-0001-5123-9861, 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 (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:	01 Nov 2022 10:00
URI:	https://orca.cardiff.ac.uk/id/eprint/89873

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