Sex with Support Vector Machines
Published 1 January 2000
Baback Moghaddam, Ming–Hsuan Yang
Citations14
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TL;DR
Nonlinear Support Vector Machines are investigated for visual sex classification with low resolution "thumbnail" faces processed from the FERET face database and the SVM performance (3.4% error) is currently the best result reported in the open literature.
Abstract
Nonlinear Support Vector Machines (SVMs) are investigated for visual sex classification with low resolution "thumbnail" faces (21by -12 pixels) processed from 1,755 images from the FERET face database. The performance of SVMs 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 ensembleRBF networks. Furthermore, the SVM performance (3.4% error) is currently the best result reported in the open literature.
Keywords
Computer Science
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