Decision dependability and its application to identity management

Authors:
Nathan D. Kalka;Nick Bartlow;Bojan Cukic
Affiliations:
West Virginia University, Morgantown, WV;West Virginia University, Morgantown, WV;West Virginia University, Morgantown, WV
Venue:
Proceedings of the 5th Annual Workshop on Cyber Security and Information Intelligence Research: Cyber Security and Information Intelligence Challenges and Strategies
Year:
2009

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Abstract

In this paper we describe decision dependability theory for binary classification problems. Each classification decision is subjected to an informal evaluation of confidence that can be attributed to it. The confidence measure emanates from the strength of evidence which supports the decision. We utilize decision dependability theory in the context of multimodal biometric identity verification systems. The confidence measure is estimated from quality and match scores of biometric samples using subjective Bayesian methodology. We demonstrate how fusion algorithms, such as Bayesian belief networks and likelihood ratio, can be complemented with decision dependability in order to predict classification errors. Furthermore, we illustrate how decision dependability can be used to rectify incorrect decisions.