LAUSR

Biometric system has been widely adopted for human verification or identification, so inherently it requires the characteristics like high security, accuracy and acceptability. However, most of existing unimodal biometric systems provide low-middle security and are vulnerable to attacks. Therefore, multimodal biometric system fuses information from multiple modalities to break these limitations. This paper presents a novel hybrid fusion model for a multimodal biometric system. The hybrid fusion model includes an improved feature fusion algorithm and a novel weighting vote strategy. It captures canonical characteristics with multi-set structure and utilizes score distribution information to help guiding decision-making. The system was examined on databases from CASIA, PolyU and SDU respectively, which provided high precision and strong robustness over previous work. Experimental results showed that the proposed approach achieved an average accuracy of 99.33%, which outperformed other fusion strategies in multimodal biometric systems.