This paper revisits the concept of an authentication machine (A-machine) that aims at identifying/verifying humans. Although A-machines in the closed-set application scenario are well understood and commonly used for access control utilizing human biometrics (face, iris, and fingerprints), open-set applications of A-machines have yet to be equally characterized. This paper presents an analysis and taxonomy of A-machines, trends, and challenges of open-set real-world applications.
This paper makes the following contributions to the area of open-set A-machines: 1) a survey of applications; 2) new novel life cycle metrics for theoretical, predicted, and operational performance evaluation; 3) a new concept of evidence accumulation for risk assessment; 4) new criteria for the comparison of A-machines based on the notion of a supporting assistant; and 5) a new approach to border personnel training based on the A-machine training mode. It offers a technique for modeling A-machines using belief (Bayesian) networks and provides an example of this technique for biometric-based e-profiling.