Redundancy and diversity measure inspired biometrics fusion
In many identification problems, finding a duplicate using biometrics is very challenging task because of the size of the database and the errors related to the core biometrics engine. Fusion of different modes of biometric system can lead to improved recognition accuracy. In a multi classifier fusion scenario, the confidence of one classifier should not only depend on its own decision confidence but also on the diffidence of other classifiers. We propose a novel solution which uses a machine learning approach to generate confidence scores for each system for every instance of decision by implicitly modelling the redundancy (all classifiers making the same decision) and diversity (each classifier making a different decision and only a subset of classifiers is right at one time)from the training data. These confidence scores are used as weights for votes and the final decision is made using weighted sum of votes. Experimental results are provided by comparing this method with more conventional methods like majority voting, and majority voting with confidence. The performance of the proposed method on NIST BSSR-1 dataset and FERET face dataset shows the efficacy of our approach measured by the accuracy improvements we are able to achieve by implicitly modelling the redundancy and diversity measures.