Machine learning facemask detection models for COVID-19

Ahmad Puzi, Asmarani and Zainuddin, Ahmad Anwar and Sahak, Rohilah and Mohamad Yunos, Muhammad Farhan Affendi and Abdul Rahman, Siti Husna and Mohd Ramly, Munirah and Maaz, Muhammad and Kaitane, Wonderful Shammah (2022) Machine learning facemask detection models for COVID-19. In: 2022 IEEE International Conference on Semiconductor Electronics (ICSE), 15-17 August 2022, Kuala Lumpur, Malaysia.

PDF - Published Version Restricted to Registered users only Download (405kB) | Request a copy

Abstract

With the breakout of the global pandemic known as COVID-19 it has forever changed ways of doing everyday things. Moreover, the discovery of new variants it has compelled regulatory authorities make the use of face mask in public places mandatory. Public places such as the public transport, shopping mall and universities where crowds of people come into contact with one another. It further exacerbates the issue by confining the masses in an indoor premise. As part of the enforcing the mandatory sop protocol work force or manpower is allocated that serve as gatekeepers to ensure the use of face mask. Due to the number of people at public places it increases the probability of human error. The solution is to incorporate the use of Artificial Intelligence that would use effective machine learning models to train and develop an effective and accurate facemask detection system. This study takes note of the existing system and develops one using the open-source library called TensorFlow to provide it with different variations of datasets that would simulate real world scenarios. With the implementation of the face mask detection system, it aims to replace manpower and allow artificial intelligence to conduct unsupervised operation that would be more efficient and effective.

Item Type:	Conference or Workshop Item (Other)
Uncontrolled Keywords:	COVID-19, Tensorflow, Mask detection system, Machine learning, Image recognition.
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Ts.Dr. Ahmad Anwar Zainuddin
Date Deposited:	22 Sep 2022 08:46
Last Modified:	22 Sep 2022 08:48
URI:	http://irep.iium.edu.my/id/eprint/100109

Actions (login required)

View Item