During the spread of the COVID-I9 pandemic in
early 2020, the WHO organization advised all people in the
world to wear face-mask to limit the spread of COVID-19. Many
facilities required that their employees wear face-mask. For the
safety of the facility, it was mandatory to recognize the identity of
the individual wearing the mask. Hence, face recognition of the
masked individuals was required. In this research, a novel
technique is proposed based on a mobile-net and Haar-like
algorithm for detecting and recognizing the masked face. Firstly,
recognize the authorized person that enters the nuclear facility
in case of wearing the masked-face using mobile-net. Secondly,
applying Haar-like features to detect the retina of the person to
extract the boundary box around the retina compares this with
the dataset of the person without the mask for recognition. The
results of the proposed modal, which was tested on a dataset
from Kaggle, yielded 0.99 accuracies, a loss of 0.08, F1.score
0.98.
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