Gender classification by principal component analysis and support vector machine
Proceedings of the 2011 International Conference on Communication, Computing & Security
Automated face identification using volume-based facial models
CGI'06 Proceedings of the 24th international conference on Advances in Computer Graphics
Gender Recognition Based On Combining Facial and Hair Features
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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Computer vision and pattern recognition systems play an important role in our lives by means of automated face detection, face and gesture recognition, and estimation of gender and age. We have developed a gender classifier with performance superior to existing gender classifiers. This paper addresses the problem of gender classification using frontal facial images. The testbed consists of 500 images (250 females and 250 males) randomly withdrawn from the FERET facial database. Independent Component Analysis (ICA) is used to represent each image as a feature vector in a low dimensional subspace. Different classifiers are studied in this lower dimensional subspace. Our experimental results show the best accuracy of 96% in gender classification by combining ICA and Support Vector Machines (SVMs).