Deep Learning Model Based on Mobile-Net with Haar-like Algorithm for Masked Face Recognition at Nuclear Facilities
Nadia.Nawwar1, Hani.Kasban2, May salama3

1Nadia.M.Nawwar*, Assistant Lecture, Egyptian Atomic Energy Authority (EAEA), Cairo, Egypt.
2Prof. Kasban, Head of Engineering and Scientific, Instruments Department, Nuclear Research Center (NRC), Egyptian Atomic Energy Authority (EAEA), Cairo, Egypt.
3May Salama, Department, Computer Science, Machine Learning and Security, University of Banha, Egypt.

Manuscript received on May 03, 2021. | Revised Manuscript received on May 06, 2021. | Manuscript published on May 30, 2021. | PP: 18-23 | Volume-10 Issue-7, May 2021 | Retrieval Number: 100.1/ijitee.G88930510721| DOI: 10.35940/ijitee.G8893.0510721
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Abstract:  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. 
Keywords: COVID-19, Deep learning, Mobile-Net, and Haar-Like.