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. Surprisingly, SVMs also out-performed human test subjects at the same task: in an experimental study involving 30 human test subjects ranging in age from mid-20s to mid-40s, the average error rate was 32% for the same ``thumbnails'' and 6.7% with high-resolution images (still nearly twice the error rate of SVMs). 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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