Hybrid deep feature generation for appropriate face mask use detection

AYDEMIR, Emrah, YALCINKAYA, Mehmet Ali, BARUA, Prabal Datta, BAYGIN, Mehmet, FAUST, Oliver, DOGAN, Sengul, CHAKRABORTY, Subrata, TUNCER, Turker and ACHARYA, U. Rajendra (2022). Hybrid deep feature generation for appropriate face mask use detection. International Journal of Environmental Research and Public Health, 19 (4).

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Official URL: https://www.mdpi.com/1660-4601/19/4/1939
Open Access URL: https://www.mdpi.com/1660-4601/19/4/1939/pdf (Published version)
Link to published version:: https://doi.org/10.3390/ijerph19041939
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    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.

    Item Type: Article
    Additional Information: ** From MDPI via Jisc Publications Router ** Licence for this article: https://creativecommons.org/licenses/by/4.0/ **Journal IDs: eissn 1660-4601 **History: published 09-02-2022; accepted 30-01-2022
    Uncontrolled Keywords: face mask detection, ResNet101, DenseNet201, transfer learning, hybrid feature selector, support vector machine
    Identification Number: https://doi.org/10.3390/ijerph19041939
    SWORD Depositor: Colin Knott
    Depositing User: Colin Knott
    Date Deposited: 11 Feb 2022 16:53
    Last Modified: 11 Feb 2022 17:00
    URI: https://shura.shu.ac.uk/id/eprint/29723

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