Learning Gender with Support Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Gender Classification of Human Faces
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FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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Proceedings of International Conference on Advances in Mobile Computing & Multimedia
Gender Recognition Based On Combining Facial and Hair Features
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
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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.