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dc.contributor.authorMaple, Carstenen_GB
dc.contributor.authorSchetinin, Vitalyen_GB
dc.date.accessioned2013-03-01T11:02:23Z
dc.date.available2013-03-01T11:02:23Z
dc.date.issued2006
dc.identifier.citationMaple, C. and Schetinin, V.; (2006) 'Using a Bayesian averaging model for estimating the reliability of decisions in multimodal biometrics', Availability, Reliability and Security, ARES 2006. The First International Conference on , pp. 7en_GB
dc.identifier.isbn0769525679
dc.identifier.doi10.1109/ARES.2006.141
dc.identifier.urihttp://hdl.handle.net/10547/270799
dc.description.abstractThe issue of reliable authentication is of increasing importance in modern society. Corporations, businesses and individuals often wish to restrict access to logical or physical resources to those with relevant privileges. A popular method for authentication is the use of biometric data, but the uncertainty that arises due to the lack of uniqueness in biometrics has lead there to be a great deal of effort invested into multimodal biometrics. These multimodal biometric systems can give rise to large, distributed data sets that are used to decide the authenticity of a user. Bayesian model averaging (BMA) methodology has been used to allow experts to evaluate the reliability of decisions made in data mining applications. The use of decision tree (DT) models within the BMA methodology gives experts additional information on how decisions are made. In this paper we discuss how DT models within the BMA methodology can be used for authentication in multimodal biometric systems.
dc.language.isoenen
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCen_GB
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1625407en_GB
dc.subjectBayes procedureen_GB
dc.subjectmultimodal biometricsen_GB
dc.subjectbiometricsen_GB
dc.titleUsing a Bayesian averaging model for estimating the reliability of decisions in multimodal biometricsen
dc.typeConference papers, meetings and proceedingsen
html.description.abstractThe issue of reliable authentication is of increasing importance in modern society. Corporations, businesses and individuals often wish to restrict access to logical or physical resources to those with relevant privileges. A popular method for authentication is the use of biometric data, but the uncertainty that arises due to the lack of uniqueness in biometrics has lead there to be a great deal of effort invested into multimodal biometrics. These multimodal biometric systems can give rise to large, distributed data sets that are used to decide the authenticity of a user. Bayesian model averaging (BMA) methodology has been used to allow experts to evaluate the reliability of decisions made in data mining applications. The use of decision tree (DT) models within the BMA methodology gives experts additional information on how decisions are made. In this paper we discuss how DT models within the BMA methodology can be used for authentication in multimodal biometric systems.


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