Feature selection for efficient gender classification
NN'10/EC'10/FS'10 Proceedings of the 11th WSEAS international conference on nural networks and 11th WSEAS international conference on evolutionary computing and 11th WSEAS international conference on Fuzzy systems
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In this work we have used non-linear Support Vector Machines (SVMs) for gender classification. The SVMis applied to triangular meshes representing human faces. In this work we rely on handful of 3-dimentional facial features which are extracted from the corresponding geometry meshes. The experimental results show that in our method the error rate is 17.44% on average. It is thought that the approach used to determine gender prior to face recognition would make an automatic face recognition system more efficient.