Partial aggregation of users for biometric scores normalization

In this paper, we analyze a new method to deal with users' biometric data, by incorporating them in an incomplete aggregation framework, to make an optimal decision for the normalization process. This latter invokes the experts' opinions concept, where the opinions are represented by the user-specific scores. This aggregation of opinions needs more experts for a trusted and informative partial consensus. The key idea of this new approach is based upon the employment of a convex mixture of users' score dispersion. This procedure is used to revise the users' scores, then to update specific statistical parameters which are the principal elements of our score normalization formula. Applying this concept to various scenarios gives an interesting outcome compared to other alternatives. The main contribution of this work for aggregation framework on native space is to introduce this new user-specific concept of scores' normalization, followed by a thorough evaluation of the performance. The validation of this approach is ensured by using a unique modality followed by the exploitation of the fusion of different modalities, to achieve a viable reliability of the final decision. The benchmarking was conducted under Nist2005, Biosecure DS2, and XM2VTS score databases.