A Multi-Level Data Fusion Approach for Gradually Upgrading the Performances of Identity Verification Systems

The aim of this paper is to propose a strategy that uses data fusion at three different levels to gradually improve the performance of an identity verification system. In a first step temporal data fusion can be used to combine multiple instances of a single (mono-modal) expert to reduce its measurement variance. If system performance after this first step is not good enough to satisfy the end-user's needs, one can improve it by fusing in a second step results of multiple experts working on the same (biometric) modality. For this approach to work, it is supposed that the respective classification errors of the different experts are (at least partially) de-correlated. Finally, if the verification system's performance after this second step is still not good enough, one will be forced to move on to the third step in which performance can be improved by using multiple experts working on different (biometric) modalities. To be useful however, these experts have to be chosen in such a way ..