An Intelligent Classifier Fusion Technique for Improved Multimodal Biometric Authentication Using Modified Dempster-shafer Rule of Combination

Multimodal biometric technology relatively is a technology developed to overcome those limitations imposed by unimodal biometric systems. The paradigm consolidates evidence from multiple biometric sources offering considerable improvements in reliability with reasonably overall performance in many applications. Meanwhile, the issue of efficient and effective information fusion of these evidences obtained from different sources remains an obvious concept that attracts research attention. In this research paper, we consider a classical classifier fusion technique, Dempster’s rule of combination proposed in Dempster-Shafer Theory (DST) of evidence. DST provides useful computational scheme for integrating accumulative evidences and possesses the potential to update the prior every time a new data is added in the database. However, it has some shortcomings. Dempster Shafer evidence combination has this inability to respond adequately to the fusion of different basic belief assignments (bbas) of evidences, even when the level of conflict between sources is low. It also has this tendency of completely ignoring plausibility in the measure of its belief. To solve these problems, this paper presents a modified Dempster’s rule of combination for multimodal biometric authentication which integrates hyperbolic tangent (tanh) estimators to overcome the inadequate normalization steps done in the original Dempster’s rule of combination. We also adopt a multi-level decision threshold to its measure of belief to model the modified Dempster Shafer rule of combination.