The biometric de-duplication problem examines whether an input biometric sample has a corresponding match in a reference database during the enrollment process. If the input biometric is deemed to have a match, then the individual is not enrolled as a new identity in the database in order to prevent duplicate entries; otherwise, a new identity profile is created and the individual is enrolled in the system. The goal is to insure that the biometric data of an individual is associated with a single identity or label in the database. De-duplication is necessary in applications that render services to enrolled individuals. However, little to no research has been performed to examine the errors involved in a de-duplication task, and their potential consequences. We formally introduce the types of errors that may arise in biometric de-duplication, and examine whether these errors can be modeled using traditional error measures such as the false match rate, false non-match rate, false positive identification rate, and false negative identification rate. Experimental results demonstrate that de-duplication error is impacted by the order biometric samples are tested for a duplicate and that traditional error measures are not adequate for estimating empirical de-duplication error.
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