{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T19:36:07Z","timestamp":1773776167297,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T00:00:00Z","timestamp":1648512000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Since December 2019, the COVID-19 pandemic has led to a dramatic loss of human lives and caused severe economic crises worldwide. COVID-19 virus transmission generally occurs through a small respiratory droplet ejected from the mouth or nose of an infected person to another person. To reduce and prevent the spread of COVID-19 transmission, the World Health Organization (WHO) advises the public to wear face masks as one of the most practical and effective prevention methods. Early face mask detection is very important to prevent the spread of COVID-19. For this purpose, we investigate several deep learning-based architectures such as VGG16, VGG19, InceptionV3, ResNet-101, ResNet-50, EfficientNet, MobileNetV1, and MobileNetV2. After these experiments, we propose an efficient and effective model for face mask detection with the potential to be deployable over edge devices. Our proposed model is based on MobileNetV2 architecture that extracts salient features from the input data that are then passed to an autoencoder to form more abstract representations prior to the classification layer. The proposed model also adopts extensive data augmentation techniques (e.g., rotation, flip, Gaussian blur, sharping, emboss, skew, and shear) to increase the number of samples for effective training. The performance of our proposed model is evaluated on three publicly available datasets and achieved the highest performance as compared to other state-of-the-art models.<\/jats:p>","DOI":"10.3390\/s22072602","type":"journal-article","created":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T21:45:51Z","timestamp":1648590351000},"page":"2602","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["An Efficient and Effective Deep Learning-Based Model for Real-Time Face Mask Detection"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6543-2520","authenticated-orcid":false,"given":"Shabana","family":"Habib","sequence":"first","affiliation":[{"name":"Department of Information Technology, College of Computer, Qassim University, Buraydah 52571, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2381-1223","authenticated-orcid":false,"given":"Majed","family":"Alsanea","sequence":"additional","affiliation":[{"name":"Computing Department, Arabeast Colleges, Riyadh 13544, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1655-8098","authenticated-orcid":false,"given":"Mohammed","family":"Aloraini","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, College of Engineering, Qassim University, Qassim 52571, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hazim Saleh","family":"Al-Rawashdeh","sequence":"additional","affiliation":[{"name":"Cyber Security Department, College of Engineering and Information Technology, Onaizah Colleges, Onaizah 56447, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2379-4451","authenticated-orcid":false,"given":"Muhammad","family":"Islam","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, College of Engineering and Information Technology, Onaizah Colleges, Onaizah 56447, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5749-8538","authenticated-orcid":false,"given":"Sheroz","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, College of Engineering and Information Technology, Onaizah Colleges, Onaizah 56447, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"108288","DOI":"10.1016\/j.measurement.2020.108288","article-title":"A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic","volume":"167","author":"Loey","year":"2021","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/S2213-2600(20)30134-X","article-title":"Rational use of face masks in the COVID-19 pandemic","volume":"8","author":"Feng","year":"2020","journal-title":"Lancet Respir. 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