Subjective Logic Based Score Level Fusion: Combining Faces and Fingerprints

Biometric systems are prone to random and systematic errors which are typically attributed to the variations in terms of inter-session data capture and intra-session variability. Furthermore, these errors cannot be defined and modeled mathematically in many cases, but we can associate them with uncertainty based on certain conditions. In such cases, one of the possible approach to improve biometric system performance is to employ multi-biometric fusion by incorporating the uncertainties. In the literature, researchers have proposed many fusion techniques, but most of these techniques do not take uncertainty into account while performing fusion. Since the decision made by uni-modal biometric comparators do not consider the uncertainty involved in such decisions, it is essential first to model the uncertainty before combining the decision from multiple uni-modal biometric systems efficiently. To this end, we propose a score level multi-biometric fusion scheme using Subjective Logic which incorporates the uncertainty of the system's information channels while fusing the scores. Extensive experiments are carried out on the multi-biometric NIST BSSR1, and the proposed scheme has indicated a superior performance with a genuine match rate of 99.02 % at a false match rate fixed to 0.01 %.