一种用于新冠疫情期间城市封锁的三层去中心化物联网生物识别架构。

Kolhar Manjur, Al-Turjman Fadi, Alameen Abdalla, Abualhaj Mosleh M

Department of Computer SciencePrince Sattam Bin Abdulaziz University Wadi Ad-Dawasir 11990 Saudi Arabia.

Research Center for AI and IoTArtificial Intelligence DepartmentNear East University 99138 Mersin Turkey.

IEEE Access. 2020 Sep 4;8:163608-163617. doi: 10.1109/ACCESS.2020.3021983. eCollection 2020.

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.

在本文中，我们构建了一个基于去中心化物联网的生物特征面部检测框架原型，用于在新冠疫情爆发期间实施封锁的城市。为了对公众流动施加限制，我们利用了基于三层边缘计算架构的面部检测技术。我们构建了一个多任务级联的深度学习框架来识别面部。对于面部检测提议，我们在诸如FDDB和WIDER FACE等各种基准数据集上与现有技术方法进行了比较。此外，我们还在三层和云计算架构上进行了关于延迟和面部检测负载的各种实验。结果表明，我们的提议比云计算架构更具优势。