1. "Coronavirus disease (COVID-19): masks", https://
www.who.int/news-room/q-a-detail/coronavirusdisease- covid-19-masks (2020).
2. Rahman, M.M., Manik, M.M.H., Islam, M.M., et al. "An automated system to limit COVID-19 using facial mask detection in smart city network", 2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1-5 (2020). DOI: 10.1109/IEMTRONICS51293.2020.9216386.
3. Zhang, K., Zhang, Z., Li, Z., et al. "Joint face detection and alignment using multitask cascaded convolutional networks", IEEE Signal Process. Lett., 23, pp. 1499- 503 (2016).
4. Cabani, A., Hammoudi, K., Benhabiles, H., et al. "MaskedFace-Net - a dataset of correctly/incorrectly masked face images in the context of COVID-19", Smart Heal., 19, 100144 (2020).
5. "Dataset of face images ickr-faces-HQ (FFHQ)" (2022), https://github.com/NVlabs/ffhq-dataset.
6. Nieto-Rodriguez, A., Mucientes, M., and Brea, V. "System for medical mask detection in the operating room through facial attributes", Iberian Conference on Pattern Recognition and Image Analysis, pp. 138|145 (2015). DOI: 10.1007/978-3-319-19390-8-16.
7. Loey, M., Manogaran, G., Taha, M., et al. "A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic", Measurement, 167, 108288 (2021).
8. Roy, B., Nandy, S., Ghosh, D., et al. "MOXA: A deep learning based unmanned approach for real-time monitoring of people wearing medical masks", Trans.Indian Natl. Acad. Eng., 5, pp. 509-518 (2020).
9. "Moxa3k dataset", https://shitty-bots-inc.github.io /MOXA/index.html (2020).
10. Waghe, S. "Medical masks dataset", https://www .kaggle.com/shreyashwaghe/medical-mask-dataset (2020).
11. Jignesh Chowdary, G., Punn, N.S., Sonbhadra, S.K., et al. "Face mask detection using transfer learning of Inceptionv3", Big Data Analytics, pp. 81-90 (Springer International Publishing, 2020).
12. Prajnasb, "Observations", https://github.com/ prajnasb/observations (2020).
13. Das, A., Ansari, M.W., and Basak, R. "COVID- 19 face mask detection using tensor flow, keras and opencv", 2020 IEEE 17th India Council International Conference (INDICON), pp. 1-5 (2020). DOI: 10.1109/INDICON49873.2020.9342585.
14. Ottakath, N., Elharrouss, O., Almaadeed, N., et al. "ViDMASK dataset for face mask detection with social distance measurement", Displays, 73, 102235 (2022).
15. Larxel, "Face mask detection",
https://www.kaggle.com/andrewmvd/face-maskdetection (2020).
16. Krizhevsky, A., Sutskever, I., and Hinton, G. "ImageNet classification with deep convolutional neural networks", Neural Inf. Process. Syst., 25, pp. 84-90 (2012).
17. Girshick, R., Donahue, J., Darrell, T., et al. "Regionbased convolutional networks for accurate object detection and segmentation", IEEE Trans. Pattern Anal. Mach. Intell., 38, pp. 142-158 (2016).
18. Dalal, N. and Triggs, B. "Histograms of oriented gradients for human detection", IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2005), 2 (2005).
19. Lowe, D.G. "Object recognition from local scaleinvariant features", Proceedings of the Seventh IEEE International Conference on Computer Vision, 2, pp. 1150-1157 (1999).
20. Viola, P. and Jones, M. "Rapid object detection using a boosted cascade of simple features", Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, pp. 511-518 (2001).
21. Ren, S., He, K., Girshick, R., et al. "Faster RCNN: Towards real-time object detection with region proposal networks", IEEE Trans. Pattern Anal. Mach. Intell., 39, pp. 1137-1149 (2015).
22. Redmon, J., Divvala, S., Girshick, R., et al. "You only look once: unified, real-time object detection", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779-788 (2016).DOI: 10.1109/CVPR.2016.91.
23. Liu, W., Anguelov, D., Erhan, D., et al. "SSD: single shot multibox detector", European Conference on Computer Vision, pp. 21-37 (2016).DOI: 10.1007/978-3-319-46448-0-2.
24. Redmon, J. and Farhadi, A. "YOLOv3: an incremental improvement", arXiv Prepr. arXiv1804.02767 (2018).
25. Adarsh, P., Rathi, P., and Kumar, M. "YOLOv3- tiny: Object detection and recognition using one stage improved model", 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 687-694 (2020).
26. Lin, T.-Y., Maire, M., Belongie, S., et al. "Microsoft coco: common objects in context", European Conference on Computer Vision (eds. Fleet, D., Pajdla, T., Schiele, B. and Tuytelaars, T.), pp. 740-755 (Springer International Publishing (2014).
27. Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. "YOLOv4: Optimal speed and accuracy of object detection", arXiv Prepr. arXiv2004.10934 (2020).
28. Asghar, M.Z., Albogamy, F.R., Al-Rakhami, M.S., et al. "Facial mask detection using depthwise separable convolutional neural network model during COVID-19 pandemic", Front. Public Heal., 10 (2022).