{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T20:41:18Z","timestamp":1778100078834,"version":"3.51.4"},"reference-count":52,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T00:00:00Z","timestamp":1690416000000},"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>Face masks are widely used in various industries and jobs, such as healthcare, food service, construction, manufacturing, retail, hospitality, transportation, education, and public safety. Masked face recognition is essential to accurately identify and authenticate individuals wearing masks. Masked face recognition has emerged as a vital technology to address this problem and enable accurate identification and authentication in masked scenarios. In this paper, we propose a novel method that utilizes a combination of deep-learning-based mask detection, landmark and oval face detection, and robust principal component analysis (RPCA) for masked face recognition. Specifically, we use pretrained ssd-MobileNetV2 for detecting the presence and location of masks on a face and employ landmark and oval face detection to identify key facial features. The proposed method also utilizes RPCA to separate occluded and non-occluded components of an image, making it more reliable in identifying faces with masks. To optimize the performance of our proposed method, we use particle swarm optimization (PSO) to optimize both the KNN features and the number of k for KNN. Experimental results demonstrate that our proposed method outperforms existing methods in terms of accuracy and robustness to occlusion. Our proposed method achieves a recognition rate of 97%, which is significantly higher than the state-of-the-art methods. Our proposed method represents a significant improvement over existing methods for masked face recognition, providing high accuracy and robustness to occlusion.<\/jats:p>","DOI":"10.3390\/s23156727","type":"journal-article","created":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T02:10:45Z","timestamp":1690510245000},"page":"6727","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":120,"title":["Innovative Hybrid Approach for Masked Face Recognition Using Pretrained Mask Detection and Segmentation, Robust PCA, and KNN Classifier"],"prefix":"10.3390","volume":"23","author":[{"given":"Mohammed","family":"Eman","sequence":"first","affiliation":[{"name":"Computer Science Department, Faculty of Computing and Artificial Intelligence, Beni Suef University, Beni-Suef 62511, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0309-8088","authenticated-orcid":false,"given":"Tarek M.","family":"Mahmoud","sequence":"additional","affiliation":[{"name":"Computer Science Department, Faculty of Science, Minia University, Minia 61519, Egypt"},{"name":"Computer Science Department, Faculty of Computers and Artificial Intelligence, University of Sadat City, Sadat City 32897, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3312-2605","authenticated-orcid":false,"given":"Mostafa M.","family":"Ibrahim","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, Faculty of Engineering, Minia University, Minia 61519, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1785-1058","authenticated-orcid":false,"given":"Tarek","family":"Abd El-Hafeez","sequence":"additional","affiliation":[{"name":"Computer Science Department, Faculty of Science, Minia University, Minia 61519, Egypt"},{"name":"Computer Science Unit, Deraya University, Minia 61765, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jain, A., Bolle, R., and Pankanti, S. 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