2023
doi: 10.12720/jait.14.2.224-232
Masked Face Detection and Recognition System Based on Deep Learning Algorithms
Hayat Al-Dmour1,*
,
Afaf Tareef1 ,
Asma Musabah Alkalbani2 ,
Awni Hammouri1 ,
Ban Alrahmani1
- 1Faculty of Information Technology, Mutah University, Mu’tah, Al Karak, Jordan
- 2Department of Information Technology, University of Technology and Applied Sciences, CAS IBRI, Muscat 516
- Manuscript receivedSeptember 21, 2022
- revisedNovember 9, 2022
- acceptedDecember 7, 2022
- publishedMarch 14, 2023
Abstract
Coronavirus (COVID-19) pandemic and its several variants have developed new habits in our daily lives. For instance, people have begun covering their faces in public areas and tight quarters to restrict the spread of the disease. However, the usage of face masks has hampered the ability of facial recognition systems to determine people’s identities for registration authentication and dependability purpose. This study proposes a new deep-learning-based system for detecting and recognizing masked faces and determining the identity and whether the face is properly masked or not using several face image datasets. The proposed system was trained using a Convolutional Neural Network (CNN) with cross-validation and early stopping. First, a binary classification model was trained to discriminate between masked and unmasked faces, with the top model achieving a 99.77% accuracy. Then, a multi-class model was trained to classify the masked face images into three labels, i.e., correctly, incorrectly, and non-masked faces. The proposed model has achieved a high accuracy of 99.5%. Finally, the system recognizes the person’s identity with an average accuracy of 97.98%. The visual assessment has proved that the proposed system succeeds in locating and matching faces.
Keywords
- COVID-19
- facemask detection
- face recognition
- AI
- deep learning
- Convolutional Neural Network (CNN)
How to Cite
Hayat Al-Dmour, Afaf Tareef, Asma Musabah Alkalbani, Awni Hammouri, and Ban Alrahmani, "Masked Face Detection and Recognition System Based on Deep Learning Algorithms," Journal of Advances in Information Technology, vol. 14, no. 2, pp. 224-232, 2023. https://doi.org/10.12720/jait.14.2.224-232
Copyright & License
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
