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Gender discriminating models from facial surface normals

Wu, Jing, Smith, W. A. P. and Hancock, E. R. 2011. Gender discriminating models from facial surface normals. Pattern Recognition 44 (12) , pp. 2871-2886. 10.1016/j.patcog.2011.04.013

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Abstract

In this paper, we show how to use facial shape information to construct discriminating models for gender classification. We represent facial shapes using 2.5D fields of facial surface normals, and investigate three different methods to improve the gender discriminating capacity of the model constructed using the standard eigenspace method. The three methods are novel variants of principal geodesic analysis (PGA) namely (a) weighted PGA, (b) supervised weighted PGA, and (c) supervised PGA. Our starting point is to define a weight map over the facial surface that indicates the importance of different locations in discriminating gender. We show how to compute the relevant weights and how to incorporate the weights into the 2.5D model construction. We evaluate the performance of the alternative methods using facial surface normals extracted from 3D range images or recovered from brightness images. Experimental results demonstrate the effectiveness of our methods. Moreover, the classification accuracy, which is as high as 97%, demonstrates the effectiveness of using facial shape information for gender classification.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Uncontrolled Keywords: Gender classification; Facial surface normals; Statistical model; Feature extraction; Principal geodesic analysis
Publisher: Elsevier
ISSN: 0031-3203
Date of Acceptance: 22 April 2011
Last Modified: 04 Jun 2017 09:03
URI: http://orca.cf.ac.uk/id/eprint/89859

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