Enhancing the Performance of Elastic Graph Matching for Face Authentication by Using Support Vector Machines

In this paper, a novel method for enhancing the performance of elastic graph matching in face authentication is proposed.The starting point 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 database of the European Union research project M2VTS. Experimental results indicate that the performance of morphological dynamic link architecture, a variant of elastic graph matching, is highly improved by using the proposed weighting technique.