Similarity Measures Fusion Using SVM Classifier for Face Authentication

In this paper, the problems of measuring similarity in LDA face space using different metrics and fusing the associated classifiers are considered. A few similarity measures used in different pattern recognition applications, including the recently proposed Gradient Direction (GD) metric are reviewed. An automatic parameter selection algorithm is then proposed for optimising the GD metric. In extensive experimentation on the BANCA database, we show that the optimised GD metric outperforms the other metrics in various conditions. Moreover, we demonstrate that by combining the GD metric and seven other metrics in the decision level using Support Vector Machines, the performance of the resulting decision making scheme consistently improves.