In this paper, several approaches that can be used to improve biometric authentication applications are proposed. The idea is inspired by the ensemble approach, i.e., the use of several classifiers to solve a problem. Compared to using only one classifier, the ensemble of classifiers has the advantage of reducing the overall variance of the system. Instead of using multiple classifiers, we propose here to examine other possible means of variance reduction (VR), namely through the use of multiple synthetic samples, different extractors (features) and biometric modalities. The scores are combined using the average operator, Multi-Layer Perceptron and Support Vector Machines. It is found empirically that VR via modalities is the best technique, followed by VR via extractors, VR via classifiers and VR via synthetic samples. This order of effectiveness is due to the corresponding degree of independence of the combined objects (in decreasing order). The theoretical and empirical findings show that the combined experts via VR techniques \emph{always} perform better than the average of their participating experts. Furthermore, in practice, \emph{most} combined experts perform better than any of their participating experts.