Combination of multiple samples utilizing identification model in biometric systems

Abstract

In some cases, the test person might be asked to provide another authentication attempt besides the first one so that combination of the two input templates might give the system more confidence if the person is genuine or impostor. Instead of simply combining the matching scores which are associated with a single person compared to the two input templates, we investigate the use of matching scores corresponding to all enrolled persons. The dependencies between scores generated by the same input templates are accounted for the proposed combination algorithm. Such combination methods can be extended to large number of classes and input templates. Since matching scores are used, the proposed methods can also be applied on arbitrary biometric modalities. The experiments are conducted on NIST BSSR1 face and FVC2002 fingerprint datasets by using both likelihood ratio and multilayer perceptron combination methods.