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.
An instruction-level simulator for IBM 3090 with VF (vector facility) has been developed for studying the performance of vector processors and their memory hierarchies. Results of a study of the locality of several large scientific applications are presented. The cache miss ratios of vectorized applications are found to be almost equal to those of their original scalar executions. Moreover, both the spatial and temporal locality of these applications (in scalar and vector executions) are strong enough to show a sufficiently high hit ratio on conventional cache structures.<<ETX>>