A Robust Multimodal Biometric System VIA Multiple Svms

Abstract

Generally single Support Vector Machine (SVM) is employed in existing multimodal biometric authentication techniques, and it assumes that whole set of the classifiers is available. But sometimes it is not possible due to some circumstances e.g. injury, some medical treatment etc. This paper includes a robust multimodal biometric authentication system that integrates FKP (Finger-Knuckle Print), face and fingerprint at matching score level fusion using multiple parallel Support Vector Machines (SVMs). Multiple SVMs are applied to overcome the problem of missing biometric modality. Every possible combination of three modalities (FKP, face and fingerprint) are taken into consideration and all combinations have a corresponding SVM to fuse the matching scores and produce the final score set for decision making. Proposed system is more flexible and robust as compared to existing multimodal biometric system with single SVM. The average accuracy of proposed system is estimated on a publicly available dataset with the use of MUBI tool(Multimodal Biometrics Integration tool) and MATLAB 2017b.