Modelling errors in a biometric re-identification system

The authors consider the problem of `re-identification´ where a biometric system answers the question `Has this person been encountered before?´ without actually deducing the person´s identity. Such a system is vital in biometric surveillance applications and applicable to biometric de-duplication. In such a system, identifiers are created dynamically as and when the system encounters an input probe. Consequently, multiple probes of the same identity may be mistakenly assigned different identifiers, whereas probes from different identities may be mistakenly assigned the same identifier. In this study, they describe a re-identification system and develop terminology as well as mathematical expressions for prediction of matching errors. Furthermore, they demonstrate that the sequential order in which the probes are encountered by the system has a great impact on its matching performance. Experimental analysis based on unimodal and multimodal faces and fingerprint scores confirms the validity of the designed error prediction model, as well as demonstrates that traditional metrics for biometric recognition fail to accurately characterise the error dynamics of a re-identification system.