Abstract: Face detection and mask detection are critical tasks in the context of public safety and compliance with mask-wearing protocols. Hence, it is important to track down whoever violated rules and regulations. Therefore, this paper aims to implement four deep learning models for face detection and face with mask detection: MobileNet, ResNet50, Inceptionv3, and VGG19. The models are evaluated based on precision and recall metrics for both face detection and face with mask detection tasks. The results indicate that the proposed model based on ResNet50 achieves superior performance in face detection, demonstrating high precision (99.4%) and recall (98.6%) values. Additionally, the proposed model shows commendable accuracy in mask detection. MobileNet and Inceptionv3 provide satisfactory results, while the proposed model based on VGG19 excels in face detection but shows slightly lower performance in mask detection. The findings contribute to the development of effective face mask detection systems, with implications for public safety.

Abdullahi Ahmed Abdirahman, Abdirahman Osman Hashi, Ubaid Mohamed Dahir, Mohamed Abdirahman Elmi and Octavio Ernest Romo Rodriguez, “Enhancing Facemask Detection using Deep learning Models” International Journal of Advanced Computer Science and Applications(IJACSA), 14(7), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140763

@article{Abdirahman2023,
title = {Enhancing Facemask Detection using Deep learning Models},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140763},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140763},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {7},
author = {Abdullahi Ahmed Abdirahman and Abdirahman Osman Hashi and Ubaid Mohamed Dahir and Mohamed Abdirahman Elmi and Octavio Ernest Romo Rodriguez}
}