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Continuous Multimodal Authentication Using Dynamic Bayesian Networks
| Citations: | 5 - 0 self |
BibTeX
@MISC{Muncaster_continuousmultimodal,
author = {Justin Muncaster and Matthew Turk},
title = {Continuous Multimodal Authentication Using Dynamic Bayesian Networks},
year = {}
}
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Abstract
In this paper we address the issue of post-login user verification using biometrics. We propose a general framework for continuously monitoring a user and his or her characteristics throughout the session in order to provide continuous verification of identity. We present a multimodal approach using dynamic Bayesian networks to account for classification uncertainty and to encode the system’s dynamic model. The system supports an extendable number of modalities that can be integrated at both the scorelevel and the decision-level. The system can also be extended to incorporate important contextual information. We provide a simple way for an operator to tailor the dynamic model using known domain knowledge. Initial tests with the system using face recognition and keystroke dynamics are promising, and we expect performance to improve as more modalities are incorporated. 1.
