Enhancing Person Identification with Score Fusion of Biometric Modalities

Abstract

Biometric systems play a vital role in identifying individuals by capitalizing on their distinctive physical or behavioral traits. However, individual biometric techniques have limitations that can impact the accuracy and reliability of identification. Multimodal biometric systems, which incorporate data from several modalities, have evolved as a solution to these problems. There are four levels of integration into multimodal biometric systems, but score-level fusion is seen as the most efficient. The fusion level used in this paper was the score level. The fusion proposal was assessed using the ORL Face and IITD Iris datasets. The suggested technique can enhance the ability to identify people in various areas.