A probabilistic risk analysis for multimodal entry control

Authors:
BošTjan Kalua;Erik Dovgan;Tea TušAr;Milind Tambe;Matja Gams
Affiliations:
Department of Intelligent Systems, Joef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia;Department of Intelligent Systems, Joef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia;Department of Intelligent Systems, Joef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia;Teamcore Research Group, University of Southern California, 3737 Watt Way, Los Angeles, CA 90089-0781, USA;Department of Intelligent Systems, Joef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

Entry control is an important security measure that prevents undesired persons from entering secure areas. The advanced risk analysis presented in this paper makes it possible to distinguish between acceptable and unacceptable entries, based on several entry sensors, such as fingerprint readers, and intelligent methods that learn behavior from previous entries. We have extended the intelligent layer in two ways: first, by adding a meta-learning layer that combines the output of specific intelligent modules, and second, by constructing a Bayesian network to integrate the predictions of the learning and meta-learning modules. The obtained results represent an important improvement in detecting security attacks.