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Recent Advances in Computational Intelligence in Defense and Security pp 391–420Cite as

Risk Assessment in Authentication Machines

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 621))

Abstract

This work introduces an approach to building a risk profiler for use in authentication machines. Authentication machine application scenarios include the security of large public events, pandemic prevention, and border crossing automation. The proposed risk profiler provides a risk assessment at all phases of the authentication machine life-cycle. The key idea of our approach is to utilize the advantages of belief networks to solve large-scale multi-source fusion problems. We extend the abilities of belief networks by incorporating Dempster-Shafer Theory measures, and report the design techniques by using the results of the prototyping of possible attack scenarios. The software package is available for researchers.

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Notes

  1. 1.

    The e-passport and e-ID are defined by the ICAO standard, and are the key components of advanced border control technologies [54]. The face was recommended as the primary biometric, mandatory for global interoperability in the passport inspection systems. Fingerprint and iris were recommended as secondary biometrics.

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Acknowledgments

We acknowledge collaboration with Dr. V. Shmerko (University of Calgary, Canada). This work was partially supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) through the Discovery grant “Biometric intelligent interfaces”, and the Government of the Province of Alberta (Queen Elizabeth II Scholarship).

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Authors and Affiliations

  1. Biometric Technology Laboratory, University of Calgary, Calgary, Canada

    S. Eastwood & S. Yanushkevich

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  1. S. Eastwood
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  2. S. Yanushkevich
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Correspondence to S. Eastwood .

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Editors and Affiliations

  1. Larus Technologies Corporation, Ottawa, Ontario, Canada

    Rami Abielmona

  2. Larus Technologies Corporation, Ottawa, Ontario, Canada

    Rafael Falcon

  3. Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada

    Nur Zincir-Heywood

  4. School of Engineering and Information Technology, University of New South Wales, Canberra, Aust Capital Terr, Australia

    Hussein A. Abbass

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Eastwood, S., Yanushkevich, S. (2016). Risk Assessment in Authentication Machines. In: Abielmona, R., Falcon, R., Zincir-Heywood, N., Abbass, H. (eds) Recent Advances in Computational Intelligence in Defense and Security. Studies in Computational Intelligence, vol 621. Springer, Cham. https://doi.org/10.1007/978-3-319-26450-9_15

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  • DOI: https://doi.org/10.1007/978-3-319-26450-9_15

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