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PRFCNN is designed to assist in extracting more salient features and prevent overfitting problems. Experiments are conducted on two benchmarked datasets, RMFD (Real-World Masked Face Dataset) and LFW Simulated Masked Face Dataset using various parameter settings. The experimental result with a minimum recognition rate of 90% accuracy promises the effectiveness of the proposed PRFCNN over the other state-of-the-art methods.<\/jats:p>","DOI":"10.3233\/jifs-220667","type":"journal-article","created":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T11:39:27Z","timestamp":1662464367000},"page":"8371-8383","source":"Crossref","is-referenced-by-count":1,"title":["Masked face recognition with principal random forest convolutional neural network (PRFCNN)"],"prefix":"10.1177","volume":"43","author":[{"given":"Lucas Chong","family":"Wei-Jie","sequence":"first","affiliation":[{"name":"Faculty of Information Science & Technology, Multimedia University, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siew-Chin","family":"Chong","sequence":"additional","affiliation":[{"name":"Faculty of Information Science & Technology, Multimedia University, 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