Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume 23, Issue 7, Pages 735-746Publisher
IEEE COMPUTER SOC
DOI: 10.1109/34.935847
Keywords
face authentication; elastic graph matching; Fisher's discriminant ratio; constrained least-squares optimization; Support Vector Machines
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A novel method for enhancing the performance of elastic graph matching in frontal face authentication is proposed. The starting point is to weigh the local similarity values 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 specifically, we propose a novel approach that reformulates Fisher's discriminant ratio to a quadratic optimization problem subject to a set of inequality constraints by combining 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.
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