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A Three Layered Decentralized IoT Biometric Architecture for City Lockdown During COVID-19 Outbreak
In this article, we have built a prototype of a decentralized IoT based biometric face detection framework for cities that are under lockdown during COVID-19 outbreaks. To impose restrictions on public movements, we have utilized face detection using three-layered edge computing architecture. We hav...
Formato: | Online Artículo Texto |
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Lenguaje: | English |
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IEEE
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8545303/ https://www.ncbi.nlm.nih.gov/pubmed/34812355 http://dx.doi.org/10.1109/ACCESS.2020.3021983 |
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collection | PubMed |
description | In this article, we have built a prototype of a decentralized IoT based biometric face detection framework for cities that are under lockdown during COVID-19 outbreaks. To impose restrictions on public movements, we have utilized face detection using three-layered edge computing architecture. We have built a deep learning framework of multi-task cascading to recognize the face. For the face detection proposal we have compared with the state of the art methods on various benchmarking dataset such as FDDB and WIDER FACE. Furthermore, we have also conducted various experiments on latency and face detection load on three-layer and cloud computing architectures. It shows that our proposal has an edge over cloud computing architecture. |
format | Online Article Text |
id | pubmed-8545303 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | IEEE |
record_format | MEDLINE/PubMed |
spelling | pubmed-85453032021-11-18 A Three Layered Decentralized IoT Biometric Architecture for City Lockdown During COVID-19 Outbreak IEEE Access Communications Technology In this article, we have built a prototype of a decentralized IoT based biometric face detection framework for cities that are under lockdown during COVID-19 outbreaks. To impose restrictions on public movements, we have utilized face detection using three-layered edge computing architecture. We have built a deep learning framework of multi-task cascading to recognize the face. For the face detection proposal we have compared with the state of the art methods on various benchmarking dataset such as FDDB and WIDER FACE. Furthermore, we have also conducted various experiments on latency and face detection load on three-layer and cloud computing architectures. It shows that our proposal has an edge over cloud computing architecture. IEEE 2020-09-04 /pmc/articles/PMC8545303/ /pubmed/34812355 http://dx.doi.org/10.1109/ACCESS.2020.3021983 Text en This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Communications Technology A Three Layered Decentralized IoT Biometric Architecture for City Lockdown During COVID-19 Outbreak |
title | A Three Layered Decentralized IoT Biometric Architecture for City Lockdown During COVID-19 Outbreak |
title_full | A Three Layered Decentralized IoT Biometric Architecture for City Lockdown During COVID-19 Outbreak |
title_fullStr | A Three Layered Decentralized IoT Biometric Architecture for City Lockdown During COVID-19 Outbreak |
title_full_unstemmed | A Three Layered Decentralized IoT Biometric Architecture for City Lockdown During COVID-19 Outbreak |
title_short | A Three Layered Decentralized IoT Biometric Architecture for City Lockdown During COVID-19 Outbreak |
title_sort | three layered decentralized iot biometric architecture for city lockdown during covid-19 outbreak |
topic | Communications Technology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8545303/ https://www.ncbi.nlm.nih.gov/pubmed/34812355 http://dx.doi.org/10.1109/ACCESS.2020.3021983 |
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