Hybrid Deep Feature Generation for Appropriate Face Mask Use Detection

Emrah Aydemir; Mehmet Ali Yalcinkaya; Prabal Datta Barua; Mehmet Baygin; Oliver Faust; Sengul Dogan; Subrata Chakraborty; Turker Tuncer; U Rajendra Acharya

doi:10.3390/ijerph19041939

Hybrid Deep Feature Generation for Appropriate Face Mask Use Detection

Int J Environ Res Public Health. 2022 Feb 9;19(4):1939. doi: 10.3390/ijerph19041939.

Authors

Emrah Aydemir¹, Mehmet Ali Yalcinkaya², Prabal Datta Barua^{3

4

5}, Mehmet Baygin⁶, Oliver Faust⁷, Sengul Dogan⁸, Subrata Chakraborty^{9

10}, Turker Tuncer⁸, U Rajendra Acharya^{11

12

13}

Affiliations

¹ Department of Management Information, College of Management, Sakarya University, Sakarya 54050, Turkey.
² Department of Computer Engineering, Engineering Faculty, Kirsehir Ahi Evran University, Kirsehir 40100, Turkey.
³ School of Management & Enterprise, University of Southern Queensland, Toowoomba, QLD 4350, Australia.
⁴ Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia.
⁵ Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia.
⁶ Department of Computer Engineering, Faculty of Engineering, Ardahan University, Ardahan 75000, Turkey.
⁷ Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 1WB, UK.
⁸ Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Turkey.
⁹ School of Science and Technology, Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, Australia.
¹⁰ Centre for Advanced Modelling and Geospatial lnformation Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia.
¹¹ Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore.
¹² Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore.
¹³ Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan.

Abstract

Mask usage is one of the most important precautions to limit the spread of COVID-19. Therefore, hygiene rules enforce the correct use of face coverings. Automated mask usage classification might be used to improve compliance monitoring. This study deals with the problem of inappropriate mask use. To address that problem, 2075 face mask usage images were collected. The individual images were labeled as either mask, no masked, or improper mask. Based on these labels, the following three cases were created: Case 1: mask versus no mask versus improper mask, Case 2: mask versus no mask + improper mask, and Case 3: mask versus no mask. This data was used to train and test a hybrid deep feature-based masked face classification model. The presented method comprises of three primary stages: (i) pre-trained ResNet101 and DenseNet201 were used as feature generators; each of these generators extracted 1000 features from an image; (ii) the most discriminative features were selected using an improved RelieF selector; and (iii) the chosen features were used to train and test a support vector machine classifier. That resulting model attained 95.95%, 97.49%, and 100.0% classification accuracy rates on Case 1, Case 2, and Case 3, respectively. Having achieved these high accuracy values indicates that the proposed model is fit for a practical trial to detect appropriate face mask use in real time.

Keywords: DenseNet201; ResNet101; face mask detection; hybrid feature selector; support vector machine; transfer learning.

MeSH terms

COVID-19* / prevention & control
Humans
Masks*
SARS-CoV-2
Support Vector Machine