Application Support Vector Machine on Face Recognition for Gender Classification

Face recognition system is capable of generating a variety of information about a person's identity quickly and accurately. One of them, face recognition is able to provide information about the gender (male or female) of each person. Gender classification has become an area of extensive research due to its increasing application in existing human-computer interaction (HCI) systems, advertising, biometrics, surveillance systems, content-based indexing and searching. This paper presents face recognition for gender classification using Support Vector Machine (SVM). Support Vector Machine are a system for efficiently training the linear learning machines which can be used for as powerful classification methodology. In this research, we have obtained face recognition accuracy rates for gender classification using Support Vector Machine (SVM) with different kernels. When training data were used 40 to 90 percent, SVM method with RBF kernel and also Polynomial kernel has achieved the same maximum accuracy that is 100 percent.