Abstract: Covid-19 is a global health emergency and a major concern in the industrial and residential sectors. It has the ability to spread leading to health problems or death. Wearing a mask in public locations and busy areas is the most effective COVID-19 prevention measure. Face recognition provides an accurate method that overcomes uncertainties such as false prediction, high cost, and time consumption, as it is understood that the primary identification for every human being is his face. As a result, masked face identification is required to solve the issue of recognizing individuals with masks in several applications such as door access systems and smart attendance systems. This paper offers an important and intelligent method to solve this issue. We propose deep transfer learning approach for masked face human identification. We created a dataset of masked-face images and examined six convolutional neural network (CNN) models on this dataset. All models show great performance in terms of very high face recognition accuracy and short training time.

Maad Shatnawi, Nahla Almenhali, Mitha Alhammadi and Khawla Alhanaee, “Deep Learning Approach for Masked Face Identification” International Journal of Advanced Computer Science and Applications(IJACSA), 13(6), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130637

@article{Shatnawi2022,
title = {Deep Learning Approach for Masked Face Identification},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130637},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130637},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {6},
author = {Maad Shatnawi and Nahla Almenhali and Mitha Alhammadi and Khawla Alhanaee}
}