Multimodal Biometric Fusion Algorithm Based on Ranking Partition Collision Theory

Score-based multimodal biometric fusion has been shown to be successful in addressing the problem of unimodal techniques′ vulnerability to attack and poor performance in low-quality data. However, difficulties still exist in how to unify the meaning of heterogeneous scores more effectively. Aside from the matching scores themselves, the importance of the ranking information they include has been undervalued in previous studies. This study concentrates on matching scores and their ranking information and suggests the ranking partition collision (RPC) theory from the standpoint of the worth of scores. To meet both forensic and judicial needs, this paper proposes a method that employs a neural network to fuse biometrics at the score level. In addition, this paper constructs a virtual homologous dataset and conducts experiments on it. Experimental results demonstrate that the proposed method achieves an accuracy of 100% in both mAP and Rank1. To show the efficiency of the proposed method in practical applications, this work carries out more experiments utilizing real-world data. The results show that the proposed approach maintains a Rank1 accuracy of 99.2% on the million-scale database. It offers a novel approach to fusion at the score level.