Quality based rank-level fusion in multibiometric systems

Multibiometric systems fuse evidences from multiple biometric sources typically resulting in better recognition accuracy. These systems can consolidate information at various levels. For systems operating in the identification mode, rank level fusion presents a viable option. In this paper, several simple but powerful modifications are suggested to enhance the performance of rank-level fusion schemes in the presence of weak classifiers or low quality input images. These modifications do not require a training phase, therefore making them suitable in a wide range of applications. Experiments conducted on a multimodal database consisting of a few hundred users indicate that the suggested modifications to the highest rank and Borda count methods significantly enhance the rank-1 accuracy. Experiments also reveal that including image quality in the fusion scheme enhances the Borda count rank-1 accuracy by ~40%.