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Evaluation of Decision Fusion Methods for Multimodal Biometrics in the Banking Application

An evaluation of decision fusion methods based on Dempster-Shafer Theory (DST) and its modifications is presented in the article, studied over real biometric data from the engineered multimodal banking client verification system. First, the approaches for multimodal biometric data fusion for verific...

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Detalles Bibliográficos
Autores principales: Szczuko, Piotr, Harasimiuk, Arkadiusz, Czyżewski, Andrzej
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8951111/
https://www.ncbi.nlm.nih.gov/pubmed/35336526
http://dx.doi.org/10.3390/s22062356
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author Szczuko, Piotr
Harasimiuk, Arkadiusz
Czyżewski, Andrzej
author_facet Szczuko, Piotr
Harasimiuk, Arkadiusz
Czyżewski, Andrzej
author_sort Szczuko, Piotr
collection PubMed
description An evaluation of decision fusion methods based on Dempster-Shafer Theory (DST) and its modifications is presented in the article, studied over real biometric data from the engineered multimodal banking client verification system. First, the approaches for multimodal biometric data fusion for verification are explained. Then the proposed implementation of comparison scores fusion is presented, including details on the application of DST, required modifications, base probability, and mass conversions. Next, the biometric verification process is described, and the engineered biometric banking system principles are provided. Finally, the validation results of three fusion approaches on synthetic and real data are presented and discussed, considering the desired outcome manifested by minimized false non-match rates for various assumed thresholds and biometric verification techniques.
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spelling pubmed-89511112022-03-26 Evaluation of Decision Fusion Methods for Multimodal Biometrics in the Banking Application Szczuko, Piotr Harasimiuk, Arkadiusz Czyżewski, Andrzej Sensors (Basel) Article An evaluation of decision fusion methods based on Dempster-Shafer Theory (DST) and its modifications is presented in the article, studied over real biometric data from the engineered multimodal banking client verification system. First, the approaches for multimodal biometric data fusion for verification are explained. Then the proposed implementation of comparison scores fusion is presented, including details on the application of DST, required modifications, base probability, and mass conversions. Next, the biometric verification process is described, and the engineered biometric banking system principles are provided. Finally, the validation results of three fusion approaches on synthetic and real data are presented and discussed, considering the desired outcome manifested by minimized false non-match rates for various assumed thresholds and biometric verification techniques. MDPI 2022-03-18 /pmc/articles/PMC8951111/ /pubmed/35336526 http://dx.doi.org/10.3390/s22062356 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Szczuko, Piotr
Harasimiuk, Arkadiusz
Czyżewski, Andrzej
Evaluation of Decision Fusion Methods for Multimodal Biometrics in the Banking Application
title Evaluation of Decision Fusion Methods for Multimodal Biometrics in the Banking Application
title_full Evaluation of Decision Fusion Methods for Multimodal Biometrics in the Banking Application
title_fullStr Evaluation of Decision Fusion Methods for Multimodal Biometrics in the Banking Application
title_full_unstemmed Evaluation of Decision Fusion Methods for Multimodal Biometrics in the Banking Application
title_short Evaluation of Decision Fusion Methods for Multimodal Biometrics in the Banking Application
title_sort evaluation of decision fusion methods for multimodal biometrics in the banking application
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8951111/
https://www.ncbi.nlm.nih.gov/pubmed/35336526
http://dx.doi.org/10.3390/s22062356
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