Support Vector Machines for Visual Gender Classification

Ming-Hsuan Yang, Baback Moghaddam

Abstract: Support vector machines (SVM) are investigated for visual gender classification with low-resolution "thumbnail" faces (21-by-12 pixels) processed from 1755 images from the FERET face database. The performance of SVM (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, SVM 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 SVM). The difference between low and high-resolution inputs with SVM was only 1% thus demonstrating a degree of robustness and relative scale invariance.

External IDs:dblp:conf/icpr/YangM00