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Learning Gender with Support Faces
May 2002 (vol. 24 no. 5)
pp. 707-711

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 percent 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 percent, thus demonstrating robustness and stability with respect to scale and degree of facial detail.

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Index Terms:
Support vector machines, gender classification, linear, quadratic, Fisher linear discriminant, RBF classifiers, face recognition
Citation:
B. Moghaddam, M.H. Yang, "Learning Gender with Support Faces," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 707-711, May 2002, doi:10.1109/34.1000244
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