Semantic Fusion for Biometric User Authentication as Multimodal Signal Processing

Today the application of multimodal biometric systems is a common way to overcome the problems, which come with unimodal systems, such as noisy data, attacks, overlapping of similarities, and nonuniversality of biometric characteristics. In order to fuse multiple identification sources simultaneously, fusion strategies can be applied on different levels. This paper presents a theoretical concept of a methodology to improve those fusions and strategies independently of their application levels. By extracting and merging certain semantic information and integrating it as additional knowledge (e.g. metadata) into the process the fusion can be potentially improved. Thus, discrepancies and irregularities of one biometric trait can be verified by another one and signal errors can be identified and corrected.