Gender Classification with Support Vector Machines
|MERL Report: ||TR2000-01: Baback Moghaddam and Ming-Hsuan Yang
Proceedings of the 4th IEEE International Conference on Face and Gesture Recognition, March, 2000
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.