SexNet: A neural network identifies sex from human faces
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Gender with Support Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Generating Image Filters for Target Recognition by Genetic Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Feature Subset Selection for Gender Classification: A Comparison Study
WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
Genetic object recognition using combinations of views
IEEE Transactions on Evolutionary Computation
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In this paper we propose a new method to classify gender from face images. In face eigenspace, certain eigenfaces encode more gender information than others. We propose a framework using genetic algorithms (GA) for gender classification. First, GAs are employed to select gender-related eigenfaces from the whole eigenface space and Support Vector Machines (SVM) are used to classify the projection coefficients. Then, GAs are used to select the female eigenfaces and the male eigenfaces from the gender eigenface subset, using SVMs to classify the error between the reconstructed images and average gender images. Finally, in order to fuse the outputs of SVMs from the previous two stages and get the classification decision, the sum rule is employed. Experimental results show that the accuracy of gender classification is 95.1%.