# Real-Time Face Mask Detection to Ensure COVID-19 Precautionary Measures in the Developing Countries
## 2 Literature Review  
Nowadays, Convolutional Neural Networks (CNN) are used in many ways to facilitate humans and also prevent their lives from damages such as fire disasters [15; 16], facial feature analysis [17; 18], healthcare [19; 20; 21], and many others fields [22; 23; 24]; in addition, in several studies, a resource-constrained device has been used to interact in real-time for human lives facilitation [25; 26]. In this work, our main focus is on face mask detection for COVID-19 prevention using a CNN. Generally, in most related publications, the focus is on face recognition and construction when wearing face masks. By contrast, this research focuses on the detection of those individuals who are not wearing face masks in public places to help in reducing the transmission of COVID-19. Scientists and researchers have proved that wearing face masks in public places significantly reduces the spreading rate of COVID-19. [PERSON] et al. [27] proposed a new method to detect the face mask wearing condition. Their trained model can classify the face mask condition into incorrect, correct, and no face mask wearing conditions. Their trained model has achieved 98.70% accuracy. [PERSON] et al. [28] proposed a YOLOv3-based framework for face detection. They have verified their method by training on the WIDER FACE [29], CelebA [30], and FDDB [31] datasets. Their model has achieved 93.9% accuracy. In [32], [PERSON] et al. proposed a GAN-based novel framework that can detect and segment the face mask from the face image and regenerate the face image using a GAN. Their trained model-generated images look like the actual images. [PERSON] et al. [33] proposed a framework to detect the special face mask in the medical room. They have used real-time image processing for detecting face masks. The objective of their work is to minimize the false-positive rate. They have achieved a 95% recall and a 5% false-positive rate for the detection of the surgical mask. [PERSON] et al. [34] proposed a framework called MRGAN to segment the microphone from the face image and used the GAN to reconstruct the segmented holes into the face image. They have trained their model on their synthetic dataset. The trained model works better than the state-of-the-art methods. [PERSON] et al. [35] proposed a face mask detector called RetinaFaceMask. Their method used a novel object-removal algorithm to remove predictions with low confidence. They have achieved 1.5% and 2.3% higher precision and 5.9% and 11.0% higher recall than state-of-the-art results on face mask and face detection, respectively. On the other hand, they also explored the performance of the proposed method on light-weight neural networks such as MobileNet. [PERSON] et al. [36] proposed deep learning mixed with a geometric-based technique to monitor social distancing and face masks in public areas. They have implemented their model on Raspberry pi4. Their model detects violations of social distancing and face mask wearing by receiving input from cameras. When a violation occurs in a specific area, the developed system sends alerts to the control room and notifies the public by an alarm. The paper presented in [37] used a transfer learning strategy for the automatic identification of persons who are not wearing a face mask. In this approach, the researchers used pre-trained InceptionV3 with fine-tuning. They used Simulated Masked Face Dataset (SMFD) for training and testing and also used image augmentation methods to increase the number of training samples for better results evaluation. In another work presented in [38], the authors used a hybrid model such as deep learning and conventional machine learning to identify a person's face mask wearing condition. Their model consists of two main components. The first one is designed to extract prominent features from facial images using Resnet50, and the second component is a different classifier used for classification such as an ensemble algorithm, Support Vector Machine (SVM), and decision trees.