Using support vector machines for face authentication based on elastic graph matching
A novel method for enhancing the performance of elastic graph matching in face authentication is proposed. Our objective is to weigh the local matching errors at the nodes of an elastic graph according to their discriminatory power. We propose a novel approach to discriminant analysis that re-formulates Fisher's linear discriminant ratio to a quadratic optimization problem subject to inequality constraints by combining statistical pattern recognition and support vector machines. The method is applied to frontal face authentication on the M2VTS database.