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% 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 ensembleRBF 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%, thus demonstrating robustness and stability with respect to scale and degree of facial detail. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Information Technology Center America; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Information Technology Center America. All rights reserved. Copyright c ©Mitsubishi Electric Information Technology Center America, 2002 201 Broadway, Cambridge, Massachusetts 02139 IEEE Transactions on Pattern Analysis and Machine Intelligene (PAMI), Vol. 24, No. 5, May 2002