Detail publikace

Biometric-Enabled Authentication Machines: A Survey of Open-Set Real-World Applications

Originální název

Biometric-Enabled Authentication Machines: A Survey of Open-Set Real-World Applications

Anglický název

Biometric-Enabled Authentication Machines: A Survey of Open-Set Real-World Applications

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

BibTex


@article{BUT119819,
  author="Shawn {Eastwood} and Vlad. {Shmerko} and Svetlana {Yanushkevich} and Martin {Drahanský} and Dmitry {Gorodnichy}",
  title="Biometric-Enabled Authentication Machines: A Survey of Open-Set Real-World Applications",
  annote="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.",
  address="NEUVEDEN",
  chapter="119819",
  doi="10.1109/THMS.2015.2412944",
  edition="NEUVEDEN",
  howpublished="print",
  institution="NEUVEDEN",
  number="2",
  volume="46",
  year="2016",
  month="april",
  pages="231--242",
  publisher="NEUVEDEN",
  type="journal article in Web of Science"
}