Gender classification using support vector machines
Ming-Hsuan Yang
Moghaddam, B.
Dept. of Comput. Sci., Illinois Univ., Urbana, IL, USA;
This paper appears in: Image Processing, 2000. Proceedings. 2000 International Conference on
Publication Date: 10-13 Sept. 2000
Volume: 2,
On page(s): 471-474 vol.2
Meeting Date: 09/10/2000 - 09/13/2000
Location: Vancouver, BC, Canada, Canada
ISSN: 1522-4880
ISBN: 0-7803-6297-7
References Cited: 14
INSPEC Accession Number: 6998643
Digital Object Identifier: 10.1109/ICIP.2000.899454
Current Version Published: 2002-08-06
Abstract
In this paper, support vector machines (SVMs) are investigated for visual gender classification with low-resolution "thumbnail" faces (21-by-12 pixels) processed from 1,755 images from the FERET face database. The performance of SVMs (3.4% error) is shown to be superior to traditional pattern classifiers (linear, quadratic, Fisher linear discriminant, nearest neighbor) as well as more modern techniques such as radial basis function (RBF) classifiers and large ensemble-RBF networks. SVMs have also been tested with high-resolution (80-by-40 pixels) images. The difference between low and high-resolution inputs with SVMs was only 1%, thus demonstrating a degree of robustness and relative scale invariance.
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