Learning gender with support faces

Nonlinear Support Vector Machines (SVMs) are investigated for appearance-based gender classification with low-resolution 'thumbnail faces processed from 1.755 images from the FERET face database. The performance of SVMs (3.4 percent 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, Furthermore, the difference in classification performance with low-resolution thumbnails (21-by-12 pixels) and the corresponding higher resolution images (84-by-48 pixels) was found to be only 1 percent, thus demonstrating robustness and stability with respect to scale and degree of facial detail.