Face recognition system using soft-output classifier fusion method

Abstract

The objective is to develop a dynamic decision selection method for face recognition system where minimum number of information about face are available to take correct decision. Statically we can develop such system where Bayesian method has been preferred in most case. It is better to fuse two or more classifier whose outputs are not highly correlated. In this work, the output of two classifiers systems are not so much correlated. We considered more than one decision for each classifier so that the correlations of the output are varied. It has been proved that the Bayesian optimal decision boundaries can be produced decision in fusion technique. It also has been proposed two methods to determine the Bayesian optimal decision are performed correctly in different database. We have proposed a different technique to calculate prior and posterior probability. Finally the fusion decision has been taken based on the probability values and it has been shown that the performance of Bayesian fusion techniques is better among the individual classifier technique. This fusion technique has been used in decision level and selected a class which is considered as correct output. Finally we have compared the performance among different classifier output and this soft-output classifier fusion method.