Face recognition using self-organizing feature maps and support vector machines

Self-organizing feature maps are topologically ordered. One develops realistic cortical structures when given approximations of the visual environment as input, and are an effective way to model the development of face recognition abilities. Support vector machines (SVMs) are classifiers, which have demonstrated high generalization capabilities. In this paper, we combine these two techniques for face recognition problem. Experiments were made on two different face databases, achieving very high recognition rates with relative low classification cost. As the results using the combination SOM/SVM were not very far from only with SVM, but the classifier cost of SOM/SVM is one-tenth of with SVM.