Selection of experts for the design of multiple biometric systems

In the biometric field, different experts are combined to improve the system reliability, as in many application the performance attained by individual experts (i.e., different sensors, or processing algorithms) does not provide the required reliability. However, there is no guarantee that the combination of any ensemble of experts provides superior performance than those of individual experts. Thus, an open problem in multiple biometric system is the selection of experts to combine, provided that a bag of experts for the problem at hand are available. In this paper we present an extensive experimental evaluation of 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. The performance of combination have been evaluated by the Area Under the Curve (AUC), and the Equal Error Rate (EER). Then, four measures have been used to characterise the performance of the individual experts included in each ensemble, namely the AUC, the EER, and two measures of class separability, i.e., the d’ and an integral separability measure. The experimental results clearly pointed out that the larger the d’ of individual experts, the higher the performance that can be attained by the combination of experts