Deployment of Facial Recognition Models at the Edge: a Feasibility Study

Zhou, X. and Keoh, S. L. (2020) Deployment of Facial Recognition Models at the Edge: a Feasibility Study. In: 2020 21st Asia Pacific Network Operations and Management Symposium (APNOMS), Daegu, South Korea, 23-25 Sep 2020, ISBN 9788995004388 (doi: 10.23919/APNOMS50412.2020.9236972)

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Model training and inference in Artificial Intelligence (AI) applications are typically performed in the cloud. There is a paradigm shift in moving AI closer to the edge, allowing for IoT devices to perform AI function onboard without incurring network latency. With the exponential increase of edge devices and data generated, capabilities of cloud computing would eventually be limited by the bandwidth and latency of the network. To mitigate the potential risks posed by cloud computing, this paper discusses the feasibility of deploying inference onboard the device where data is being generated. A secure access management system using MobileNet facial recognition was implemented and the preliminary results showed that the deployment at the edge outperformed the cloud deployment in terms of overall response speed while maintaining the same recognition accuracy. Thus, management of the automated deployment of inference models at the edge is required.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Keoh, Dr Sye Loong
Authors: Zhou, X., and Keoh, S. L.
College/School:College of Science and Engineering > School of Computing Science
Research Group:GLASS
Copyright Holders:Copyright © 2020, IEEE
First Published:First published in 2020 21st Asia-Pacific Network Operations and Management Symposium (APNOMS)
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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