{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:44:00Z","timestamp":1760150640930,"version":"build-2065373602"},"reference-count":74,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,23]],"date-time":"2023-12-23T00:00:00Z","timestamp":1703289600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"AGE-WELL Inc."},{"name":"Canada Research Chairs (CRC) Program"},{"name":"Natural Sciences and Engineering Research Council of Canada (NSERC)"},{"name":"NSERC CREATE HeRo fellowship"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Wearing masks in indoor and outdoor public places has been mandatory in a number of countries during the COVID-19 pandemic. Correctly wearing a face mask can reduce the transmission of the virus through respiratory droplets. In this paper, a novel two-step deep learning (DL) method based on our extended ResNet-50 is presented. It can detect and classify whether face masks are missing, are worn correctly or incorrectly, or the face is covered by other means (e.g., a hand or hair). Our DL method utilizes transfer learning with pretrained ResNet-50 weights to reduce training time and increase detection accuracy. Training and validation are achieved using the MaskedFace-Net, MAsked FAces (MAFA), and CelebA datasets. The trained model has been incorporated onto a socially assistive robot for robust and autonomous detection by a robot using lower-resolution images from the onboard camera. The results show a classification accuracy of 84.13% for the classification of no mask, correctly masked, and incorrectly masked faces in various real-world poses and occlusion scenarios using the robot.<\/jats:p>","DOI":"10.3390\/computers13010007","type":"journal-article","created":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T20:48:37Z","timestamp":1703450917000},"page":"7","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Robust Face Mask Detection by a Socially Assistive Robot Using Deep Learning"],"prefix":"10.3390","volume":"13","author":[{"given":"Yuan","family":"Zhang","sequence":"first","affiliation":[{"name":"Autonomous Systems and Biomechatronics Laboratory (ASBLab), Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meysam","family":"Effati","sequence":"additional","affiliation":[{"name":"Autonomous Systems and Biomechatronics Laboratory (ASBLab), Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3918-2692","authenticated-orcid":false,"given":"Aaron Hao","family":"Tan","sequence":"additional","affiliation":[{"name":"Autonomous Systems and Biomechatronics Laboratory (ASBLab), Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7080-6857","authenticated-orcid":false,"given":"Goldie","family":"Nejat","sequence":"additional","affiliation":[{"name":"Autonomous Systems and Biomechatronics Laboratory (ASBLab), Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada"},{"name":"KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON M5G 2A2, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,23]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2022, April 06). 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