Designing multiple biometric systems: Measures of ensemble effectiveness

Abstract

An “expert” for biometric authentication systems is made up of three components: a biometric sensor, a feature extraction module, and a matching algorithm. As in many application the performance attained by individual experts does not provide the required reliability, improvements can be provided by the combination of different experts. However, there is no guarantee that the combination of any ensemble of experts provides superior performance than those of individual experts. Thus, it would be useful to have some measures to select the experts to be combined. In this paper, we present an experimental evaluation of the correlation between the measures of ensemble effectiveness of the experts to be combined, and the final performance achieved by the combined system. These measures of ensemble effectiveness are based on four performance measures of the individual experts, namely the AUC, the EER, the d ′ , and a score dissimilarity measure. Then, we considered four combination methods, i.e. the mean rule, the product rule, the dynamic score selection technique, and a linear combination based on the linear discriminant analysis. Reported results show that the measure of ensemble effectiveness based on the d ′ is the most effective to select the members of an ensemble of experts.