Proceedings of the 2021 Sixth International Conference on Research in Intelligent and Computing

DOI: http://dx.doi.org/10.15439/2021R13

Citation: Proceedings of the 2021 Sixth International Conference on Research in Intelligent and Computing, Vijender Kumar Solanki, Nguyen Ho Quang (eds). ACSIS, Vol. 27, pages 71–76 (2021)

Full text

Abstract. Over the last year, the correct wearing of facial masks in public is still a relevant matter in the fight against the COVID-19 pandemic. A popular approach that helps regulate the situation by global researchers is building smart systems for face mask detection. Following such spirit, this paper will contribute to the literature in two main aspects: \\ (1) We first propose a new face mask detector model using the state-of-the-art RetinaFace for face localization in populous regions and the ResNet50V1 classifier to group the faces under 3 categories: correctly-worn, incorrectly-worn and no-masks-worn. \\ (2) In order to select the ResNet50V1 as the backbone for the final model, we also analyzed its performance in accordance with another 3 classifiers on a face mask dataset beforehand. Performance metrics from the test phase have shown that our detector achieved the best accuracy among all the works compared, with $94,59$\\% on one test dataset and a less satisfactory $69.6$\\% on another due to certain characteristics of the set. The code is available at: \url{https://github.com/barbatoz0220/Densely-populated-FMD.git}

References

1. Warwick McKibbin and Roshen Fernando. The economic impact of covid-19. Economics in the Time of COVID-19, 45, 2020.
2. World Health Organization et al. World health organization coronavirus disease 2019 (covid-19) situation report, 2020.
3. Worldometers. Coronavirus updates (live) - covid-19 coronavirus pandemic. Available at https://www.worldometers.info/coronavirus/.
4. Steffen E Eikenberry, Marina Mancuso, Enahoro Iboi, Tin Phan, Keenan Eikenberry, Yang Kuang, Eric Kostelich, and Abba B Gumel. To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the covid-19 pandemic. Infectious Disease Modelling, 5:293–308, 2020.
5. Mei Wang and Weihong Deng. Deep face recognition: A survey. Neurocomputing, 429:215–244, 2021.
6. Connor Shorten, Taghi M Khoshgoftaar, and Borko Furht. Deep learning applications for covid-19. Journal of big Data, 8(1):1–54, 2021.
7. Elliot Mbunge, Sakhile Simelane, Stephen G Fashoto, Boluwaji Akinnuwesi, and Andile S Metfula. Application of deep learning and machine learning models to detect covid-19 face masks-a review. Sustainable Operations and Computers, 2021.
8. Mohamed Loey, Gunasekaran Manogaran, Mohamed Hamed N Taha, and Nour Eldeen M Khalifa. Fighting against covid-19: A novel deep learning model based on yolo-v2 with resnet-50 for medical face mask detection. Sustainable Cities and Society, page 102600, 2020.
9. Mohamed Loey, Gunasekaran Manogaran, Mohamed Hamed N Taha, and Nour Eldeen M Khalifa. 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.
10. Srinivasa Raju Rudraraju, Nagender Kumar Suryadevara, and Atul Negi. Face mask detection at the fog computing gateway. In 2020 15th Conference on Computer Science and Information Systems (FedCSIS), pages 521–524. IEEE, 2020.
11. Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint https://arxiv.org/abs/1704.04861, 2017.
12. Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen. Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4510–4520, 2018.
13. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
14. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Identity mappings in deep residual networks. In European conference on computer vision, pages 630–645. Springer, 2016.
15. Ashish Jangra. Face mask 12k images dataset - 12k images divided in training testing and validation directories, May 2020. Available at https://www.kaggle.com/ashishjangra27/face-mask-12k-images-dataset.
16. Adnane Cabani, Karim Hammoudi, Halim Benhabiles, and Mahmoud Melkemi. Maskedface-net – a dataset of correctly/incorrectly masked face images in the context of covid-19. Smart Health, 19:100144, 2021. Available at https://github.com/cabani/MaskedFace-Net.
17. Detect faces and determine whether people are wearing mask. Available at https://github.com/AIZOOTech/FaceMaskDetection.
18. Face mask detection at the edge. Available at https://neuralet.com/face-mask-detection-at-the-edge/#showcase-section-4.
19. Larxel. Face mask detection, May 2020. Available at https://www.kaggle.com/andrewmvd/face-mask-detection.
20. Rahul. Masked face data, May 2020. Available at https://www.kaggle.com/rahulmangalampalli/mafa-data.
21. Jiankang Deng, Jia Guo, Evangelos Ververas, Irene Kotsia, and Stefanos Zafeiriou. Retinaface: Single-shot multi-level face localisation in the wild. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5203–5212, 2020.
22. Howard Sandler. Mobilenetv2: The next generation of on-device computer vision networks. Available at https://ai.googleblog.com/2018/04/mobilenetv2-next-generation-of-on.html.