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Learning Gender with Support Faces

Date:
January 2002

MERL Contact: Joseph Katz

Authors: Baback Moghaddam and Ming-Hsuan Yang

Where Published: IEEE Transactions on Pattern Analysis & Machine Intelligence, Vol. 64, No. 5, May 2002

Abstract: 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 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%, thus demonstrating robustness and stability with respect to scale and degree of facial detail.

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