3d8d135818d1675848a-7a03 20240121024148079 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Recent Technology and Engineering (IJRTE) IJRTE 22773878 10.35940/ijrte.2277-3878 https://www.ijrte.org/ 01 30 2024 12 5 Department of Computer Science and Engineering, Anhui University of Science and Technology, Huainan (Anhui), China. Iqra Yamin https://orcid.org/0009-0009-8126-1934 Yang Gaoming https://orcid.org/0000-0002-7666-1038 Department of Computer Science and Engineering, Anhui University of Science and Technology, Huainan (Anhui), China. Marcel BAKALA https://orcid.org/0009-0004-1597-2897 Department of Computer Science and Engineering, Anhui University of Science and Technology, Huainan (Anhui), China. Muhammad Asad Yamin https://orcid.org/0009-0006-8559-0645 Department of Computational Science and Engineering, University of Rostock Germany. Usama Masood Department of Mechanical Engineering, Anhui University of Science and Technology, Huainan (Anhui), China. Recently, face recognition technology has become increasingly important for safety purposes. Masks are now required in most countries and are increasingly used. Public health professionals advise people to conceal their facial features outdoors to reduce COVID-19 transmission by 65%. Detecting people without masks on their faces is crucial. This has become widely used as face recognition outperforms PINs, passwords, fingerprints, and other safety verification methods. Sunglasses, scarves, caps, and makeup have made facial identification harder in recent decades. Thus, such masks impact facial recognition performance. Face masks also make traditional technology for facial recognition ineffective for face authorization, security checks, school monitoring, and cellphone and laptop opening. Thus, we proposed Masked Facial Recognition (MFR) to recognize veiled and exposed-face people so they don't need to remove their masks to verify themselves. This deep computing model was trained with Inception Res Network V1. CASIA is responsible for preparing pictures and using LFW to validate models. Dlib creates masked datasets utilizing vision algorithms. About 96% accuracy was achieved using our three models that were trained. Thus, covered and uncovered recognition of faces and detection techniques in security and safety verification might easily be used. These systems can be used in various settings, such as airports, train stations, and other public places, to enhance security and prevent crime. Overall, deep learning within face recognition technology has significant potential for improving safety and security in various settings. 01 30 2024 1 11 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true International Journal of Recent Technology and Engineering (IJRTE) No, I did not receive. No conflicts of interest to the best of our knowledge. No, the article does not require ethical approval and consent to participate with evidence. Not relevant. All authors have equal participation in this article. 10.35940/ijrte.E7970.12050124 https://www.ijrte.org/portfolio-item/E79700112524/