{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T00:50:05Z","timestamp":1701478205395},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684444","type":"print"},{"value":"9781643684451","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,30]]},"abstract":"<jats:p>Against the backdrop of the COVID-19 pandemic, the ubiquitous adoption of masks has caused significant hurdles to conventional face recognition technologies and their applications. In order to address these problems, a great deal of research related to masked face recognition has been put forward. Although masked face recognition has made great progress, the review or summary about this field is scanty. Therefore, this paper proposed a survey on masked face recognition. Firstly, the datasets of MFR and the mainstream deep learning methods are introduced. In addition, some common evaluation metrics are explained. Finally, this paper discusses the potential challenges of MFR and make conclusion.<\/jats:p>","DOI":"10.3233\/faia230860","type":"book-chapter","created":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T15:55:10Z","timestamp":1701446110000},"source":"Crossref","is-referenced-by-count":0,"title":["A Survey on Masked Face Recognition Amid the COVID-19 Pandemic"],"prefix":"10.3233","author":[{"given":"Shanmei","family":"Zhong","sequence":"first","affiliation":[{"name":"Business school, Central South University, Changsha, Hunan, 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Advances in Artificial Intelligence, Big Data and Algorithms"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA230860","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T15:55:12Z","timestamp":1701446112000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA230860"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,30]]},"ISBN":["9781643684444","9781643684451"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia230860","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,30]]}}}