Biometric Recognition in a Multi-sample Multi-Subject Facial Image Database: The 1:M:N System Model

Abstract

Over the last 50 years, biometric recognition has advanced from localized “identity verification” applications [GU77][RY74] to include large-scale systems in which “a determination is made as to the identity of an individual independently of any information supplied by the individual” [GU77]. Models for estimating and expressing system error rates (both false matches and false non-matches) have been largely limited to so-called “1-to-1” and “1-to-N” systems in which each identity is represented by only one enrolled reference [Gr21]. In this paper, we create a highly simplified simulation model for a common current situation in which each known identity record has multiple stored references. We call this the “1:M:N” model and show that both DET and CMC performance depend upon the number of identities and images per identity, not simply the total number of references images, as usually assumed. Although trialed here on very simple decision policies, this model will be extended in future work to more complex decision criteria.