An online learning network for biometric scores fusion
In design of a multibiometric system, a major concern is the learning cost in terms of computation complexity and memory usage due to large data size. In this paper, we propose an online learning network to circumvent the computational problem. Although conventional online learning algorithms can be adopted, their optimization of the fitting distance residuals does not meet the actual classification error requirement. A direct optimization to the classification performance is thus desired. Since the proposed classification-based formulation involves a class-specific weight which varies according to the total number of genuine-users and imposters, an online learning formulation becomes non-trivial. Extensive empirical evaluations on publicly available data sets show promising potential of the proposed method in terms of fusion verification accuracy and computational cost.
online learning multibiometrics score level fusion error rate pattern classification extreme learning machine (elm)