Gender classification using support vector machines
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.