A Three Layered Decentralized IoT Biometric Architecture for City Lockdown During COVID-19 Outbreak
Manjur Kolhar, Fadi Al‐Turjman, Abdalla Alameen, Mosleh M. Abualhaj
Abstract
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.