https://hdl.handle.net/1959.11/52325
Title: | Hybrid Deep Feature Generation for Appropriate Face Mask Use Detection |
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Contributor(s): | Aydemir, Emrah (author); Yalcinkaya, Mehmet Ali (author); Barua, Prabal Datta (author); Baygin, Mehmet (author); Faust, Oliver (author); Dogan, Sengul (author); Chakraborty, Subrata (author) ; Tuncer, Turker (author); Acharya, U Rajendra (author) |
Publication Date: | 2022 |
Early Online Version: | 2022-02-09 |
Open Access: | Yes |
DOI: | 10.3390/ijerph19041939 |
Handle Link: | https://hdl.handle.net/1959.11/52325 |
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. |
Publication Type: | Journal Article |
Source of Publication: | International Journal of Environmental Research and Public Health, 19(4), p. 1-16 |
Publisher: | MDPI AG |
Place of Publication: | Switzerland |
ISSN: | 1660-4601 1661-7827 |
Fields of Research (FoR) 2020: | 460102 Applications in health 461103 Deep learning 460308 Pattern recognition |
Socio-Economic Objective (SEO) 2020: | 209999 Other health not elsewhere classified 280115 Expanding knowledge in the information and computing sciences |
Peer Reviewed: | Yes |
HERDC Category Description: | C1 Refereed Article in a Scholarly Journal |
Appears in Collections: | Journal Article School of Science and Technology |
Files in This Item:
File | Description | Size | Format | |
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openpublished/HybridChakraborty2022JournalArticle.pdf | Published version | 2.13 MB | Adobe PDF Download Adobe | View/Open |
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