Error Level Fusion of Multimodal Biometrics

This paper presents a multimodal biometric system based on error level fusion. Two error level fusion strategies, one involving the Choquet integral and another involving the t-norms are proposed. The first strategy fully exploits the non additive aspect of the integral that accounts for the dependence or the overlapping information between the error rates FAR's and FRR's of each biometric modality under consideration. A hybrid learning algorithm using combination of Particle Swarm Optimization, Bacterial Foraging and Reinforcement learning is developed to learn the fuzzy densities and the interaction factor. The second strategy employs t-norms that require no learning. The fusion of the error rates using t-norms is not only fast but results in very good performance. This sort of fusion is a kind of decision level fusion as the error rates are derived from the decisions made on individual modalities. The experimental evaluation on two hand based datasets and two publically available datasets confirms the utility of the error level fusion.