Abstract:

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 also out-performed human test subjects at the same task: in a perception study with 30 human test subjects, ranging in age from mid-20s to mid-40s, the average error rate was found to be 32% for the \"thumbnails\" and 6.7% with higher resolution images. The difference in performance between low and high resolution tests with SVMs was only 1%, demonstrating robustness and relative scale invariance for visual classification.