A novel method for enhancing the performance of elastic graph matching in frontal face authentication is proposed. The starting point i s t o w eigh the local similarity v alues at the nodes of an elastic graph according to their discriminatory power. Powerful and well-established optimization techniques are used to derive the weights of the linear combination. More speci cally, w e propose a novel approach that reformulates Fisher's discriminant ratio to a quadratic optimization problem subject to a set of inequality constraints by c o m bining statistical pattern recognition and Support Vector Machines. Both linear and nonlinear Support Vector Machines are then constructed to yield the optimal separating hyperplanes and the optimal polynomial decision surfaces, respectively. The method has been applied to frontal face authentication on the M2VTS database. Experimental results indicate that the performance of morphological elastic graph matching, is highly improved by using the proposed weighting technique.
Several works about the EGM algorithm are reported in the literature: Tefas et al. REF reformulated the Fisher's discriminant ratio to a quadratic optimisation problem subject to a set of inequality constraints, finding the optimal separating hyperplanes and the polynomial decision surfaces.
Using support vector machines to enhance the performance of elastic graph matching for frontal face authentication
IEEE Trans. Pattern Anal. Mach. Intell.
IEEE Trans. Pattern Anal. Mach. Intell.
Computer Science