Risk-Adapted Access Control with Multimodal Biometric Identification

Gabor Werner () and Laszlo Hanka
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Gabor Werner: Obuda University, Applied Biometric Institute, Budapest, Hungary
Laszlo Hanka: Obuda University, Institute of Mechatronics and Autotechnics, Budapest, Hungary

Interdisciplinary Description of Complex Systems - scientific journal, 2020, vol. 18, issue 3, 327-336

Abstract: The presented article examines the background of biometric identification. As a technical method of authentication, biometrics suffers from some limitations. These limitations are due to human nature, because skin, appearance and behavior changes more or less continuously in time. Changing patterns affect quality and always pose a significantly higher risk. This study investigated risk adaption and the integration of the mathematical representation of this risk into the whole authentication process. Several biometrical identification methods have been compared in order to find an algorithm of a multimodal biometric identification process as a possible solution to simultaneously improve the rates of failed acceptations and rejections. This unique solution is based on the Adaptive Neuro-Fuzzy Inference System and the Bayesian Theorem.

Keywords: multimodal biometrics; artificial intelligence; ANFIS; risk management (search for similar items in EconPapers)
JEL-codes: Z13 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:zna:indecs:v:18:y:2020:i:3:p:337-336

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