Sex with Support Vector Machines

Part of Advances in Neural Information Processing Systems 13 (NIPS 2000)

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Baback Moghaddam, Ming-Hsuan Yang


Nonlinear Support Vector Machines (SVMs) are investigated for visual sex classification with low resolution "thumbnail" faces (21- by-12 pixels) processed from 1,755 images from the FE RET face database. The performance of SVMs is shown to be superior to traditional pattern classifiers (Linear, Quadratic, Fisher Linear Dis(cid:173) criminant, Nearest-Neighbor) as well as more modern techniques such as Radial Basis Function (RBF) classifiers and large ensemble(cid:173) RBF networks. Furthermore, the SVM performance (3.4% error) is currently the best result reported in the open literature.