Face Mask Recognition to Identify People Wearing Masks to Support Covid-19 Prevention Policies

Yilmaz, Hasan Ömer and Uslan, Volkan and Şeker, Hüseyin (2021) Face Mask Recognition to Identify People Wearing Masks to Support Covid-19 Prevention Policies. In: 2021 the 5th International Conference on Advances in Artificial Intelligence (ICAAI 2021), 20-22 November 2021.

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Nowadays, Covid-19 pandemic has penetrated all the human beings daily life. One of the most common and strongest way to prevent spread is wearing a face mask in public places. People gained a new term to their vocabulary; “new normal”. The new normal contains wearing a face mask as well. Therefore, a face mask recognition system is a vital need for helping daily life processes. This paper acquaints a face mask recognition to identify masked and unmasked faces to support Covid-19 policies. The face mask recognition in this paper developed by deep learning algorithm using the CNN architecture VGG-16. Our results suggest that deep learning-based method achieved high accuracy (99%) in both the validation and testing datasets.

Item Type: Conference or Workshop Item (Paper)
10 August 2021Accepted
22 November 2021Published
Uncontrolled Keywords: Face Mask Recognition, Deep Learning, Covid-19
Subjects: CAH01 - medicine and dentistry > CAH01-01 - medicine and dentistry > CAH01-01-01 - medical sciences (non-specific)
CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Huseyin Seker
Date Deposited: 31 Aug 2021 09:38
Last Modified: 09 Aug 2023 15:52
URI: https://www.open-access.bcu.ac.uk/id/eprint/12124

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