Analyzing State-of-the-Art Techniques for Fusion of Multimodal Biometrics

Abstract

Multimodal systems used for face recognition can be broadly classified into three categories: score level fusion, decision level fusion, and feature level fusion. In this paper, we have analyzed the performance of the three categories on various standard public databases such as Biosecure DS2, FERET, VidTIMIT, AT&T, USTB I, USTB II, RUsign, and KVKR. From our analysis, we found that score level fusion approach can effectively fuse multiple biometric modalities, and is robust to operate in less constrained conditions. In the decision fusion scheme, each decision is made after the improvement of the classifier confidence hence the recognition rate obtained is less compared to score level fusion. Feature level fusion requires less information and performs better than decision level fusion, but its recognition rate is less compared to score level fusion.