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Learning gender with support faces

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2 Author(s)
B. Moghaddam ; Mitsubishi Electr. Res. Labs., Cambridge, MA, USA ; Ming-Hsuan Yang

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 REcognition Technology) 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" (21×12 pixels) and the corresponding higher-resolution images (84×48 pixels) was found to be only 1%, thus demonstrating robustness and stability with respect to scale and the degree of facial detail

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:24 ,  Issue: 5 )