## 3 Related Works
### Summary of Previous Review Papers on Mask-Face Detection Methodologies  
Research on mask-face detection emerged in recent years in varied datasets, model types, evaluation criteria and applied systems. This motivated several researchers to review and summarize different approaches to establish reliable guidelines for future development. In this section, we discuss in detail and summarize these previous literature reviews on mask-face detection. Since mask-face detection is an application of object detection algorithms, [PERSON] et al. [4] first divided mask-face detection models into machine learning-based algorithms and deep learning-based algorithms, then divided each category of models further. Traditional methods were divided into Viola-Jones detectors [8], HOG-based detectors, etc. Methods based on deep learning were classified as CNN, GAN and other deep neural networks. Additionally, the reviews stressed the importance of the remaining image resolution during detection, the scarcity of diverse datasets and classification of objects, incorporated issues such as the diversity of datasets and mask types, different mask wearing conditions, and reconstruction of masked faces for future improvements. In the review provided in [5], the authors introduced state-of-the-art neural network models and datasets used in mask-face detection comprehensively and evaluated the prediction accuracy. They also detailed the shortcomings of different models. As the conclusion, this review proposed future research directions for detecting mask-wearing conditions in real scenarios; highlighted the demand for a shared mask database to develop a high-accuracy and real-time automatic mask-face detection system; and proposed the fitting of a deeper and wider range of deep learning architectures of more training parameters for the mask-face detection task, such as Mask R-CNN [9], Inception-v4 [10], Xception [11], etc. In another study [6], face-mask detection models were categorized into one-stage detection, two-stage detection, and feature extraction. The study summarized and analyzed the real-time and mask-face detection technologies and compared the advantages and challenges among different structures. After reviewing all the research, it was found that the utilization rate of deep learning surged after the COVID-19 outbreak. In addition, the reviews proposed that the quality of datasets can be improved by removing images with insufficient light, and by combining different detection models with systems that can detect whether a sufficient physical distance is maintained between people. In [7], [PERSON] et al. introduced several techniques of mask-face detection and briefly analyzed their drawbacks according to multi-stage and single-stage detection. They divided multi-stage detection models into CNN, R-CNN [12], Fast R-CNN [13], Faster R-CNN [14], and Mask R-CNN [9]. Similarly, YOLO [15] was introduced as single-stage detection. However, the review mainly discussed the architectures of general object detection models, rather than focusing on the characteristics of mask-face detection itself. In the above literature reviews, the researchers all first extended face-mask detection to a general object detection task. By collecting the reported datasets, classifying and analyzing different model architectures, studies put forward multiple prospects for improving mask-face detection models in the continuous epidemic. Nevertheless, these studies shared the limitation of paying little attention to distinguishing the mask-face categories and failing to focus on the techniques of detecting the third class of incorrect mask-wearing. Moreover, none of the previous reviews thoroughly explored the application of mask-face detection models to hardware systems. To fill the gaps left by the above mentioned reviews, we divide the research in mask-face detection into two-class and three-class reviews in the next section and provide another mask-face detection hardware system review in Section 5.