Abstract
Medical researchers around the globe provide evidence that COVID-19 pandemic diseases transmitted through droplets and respirators of respiratory aerosols and wearing a face mask is an efficient infection control recommendation process. In addition, many public and private service providers demand that consumers use the service only if they wear masks properly. However, a few research studies have been found on face mask detection based on the technology of Artificial Intelligence (AI) and Image Processing. In this article, we propose, MobileNet Mask, which is a deep learning-based multi-phase face mask detection model for preventing human transmission of SARS-CoV-2. Two different face mask datasets along with more than 5,200 images have been utilized to train and test the model for detecting with and without a face mask from the images and video stream. Experiment results show that with 770 validation samples MobileNet Mask achieves an accuracy of ~ 93% whereas with 276 validation samples it attains an accuracy of nearly ~ 100%. Lastly, we also discuss the possibility of implementing our proposed MobileNet Mask model on light-weighted computing devices such as mobile or embedded devices. Besides, this proposed model also introduces frontier technologies to support the efforts of government and public health guidelines with anticipation of implementing mandatory face mask regulations all over the world.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Asadi, S., Wexler, A.S., Cappa, C.D., Barreda, S., Bouvier, N.M., Ristenpart, W.D.: Aerosol emission and superemission during human speech increase with voice loudness. Scientific Reports. 9, 2348 (2019). https://doi.org/10.1038/s41598-019-38808-zhttps://doi.org/10.1038/s41598-019-38808-z
Driessche, K.V., Hens, N., Tilley, P., Quon, B.S., Chilvers, M.A., de Groot, R., Cotton, M.F., Marais, B.J., Speert, D.P., Zlosnik, J.E.A.: Surgical Masks Reduce Airborne Spread of Pseudomonas aeruginosa in Colonized Patients with Cystic Fibrosis. Am. J. Respir. Crit. Care Med. 192, 897–899 (2015). https://doi.org/10.1164/rccm.201503-0481LEhttps://doi.org/10.1164/rccm.201503-0481LE
Kumar, A., Kaur, A., Kumar, M.: Face detection techniques: a review. Artif. Intell. Rev. 52, 927–948 (2019). https://doi.org/10.1007/s10462-018-9650-2https://doi.org/10.1007/s10462-018-9650-2
Loey, M., Manogaran, G., Taha, M.H.N., Khalifa, N.E.M.: 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). https://doi.org/10.1016/j.measurement.2020.108288https://doi.org/10.1016/j.measurement.2020.108288
Li, C., Wang, R., Li, J., Fei, L.: Face Detection Based on YOLOv3. In: Jain, V., Patnaik, S., Popențiu Vlădicescu, F., and Sethi, I.K. (eds.) Rec. Trends Intell. Comput. Commun. Devices. pp. 277–284. Springer, Singapore (2020). https://doi.org/10.1007/978-981-13-9406-5_34.
Nieto-Rodríguez, A., Mucientes, M., Brea, V.M.: System for medical mask detection in the operating room through facial attributes. In: Paredes, R., Cardoso, J.S., and Pardo, X.M. (eds.) Pattern recognition and image analysis. pp. 138–145. Springer International Publishing, Cham (2015). https://doi.org/10.1007/978-3-319-19390-8_16.
Ejaz, Md.S., Islam, Md.R., Sifatullah, M., Sarker, A.: Implementation of principal component analysis on masked and non-masked face recognition. In: 2019 1st international conference on advances in science, engineering and robotics technology (ICASERT). pp. 1–5 (2019). https://doi.org/10.1109/ICASERT.2019.8934543.
Wang, Z., Wang, G., Huang, B., Xiong, Z., Hong, Q., Wu, H., Yi, P., Jiang, K., Wang, N., Pei, Y., Chen, H., Miao, Y., Huang, Z., Liang, J.: Masked face recognition dataset and application. arXiv:2003.09093 [cs]. (2020)
Jiang, M., Fan, X., Yan, H.: RetinaMask: a face mask detector. arXiv:2005.03950 [cs]. (2020)
prajnasb: prajnasb/observations. (2020). https://github.com/prajnasb/observations. Last accessed 22 July 2020
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: MobileNetV2: inverted residuals and linear bottlenecks. In: 2018 IEEE/CVF conference on computer vision and pattern recognition. pp. 4510–4520 (2018). https://doi.org/10.1109/CVPR.2018.00474.
Carneiro, T., Medeiros Da NóBrega, R.V., Nepomuceno, T., Bian, G.-B., De Albuquerque, V.H.C., Filho, P.P.R.: Performance analysis of Google colaboratory as a tool for accelerating Deep Learning Applications. IEEE Access. 6, 61677–61685 (2018). https://doi.org/10.1109/ACCESS.2018.2874767
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Dey, S.K., Howlader, A., Deb, C. (2021). MobileNet Mask: A Multi-phase Face Mask Detection Model to Prevent Person-To-Person Transmission of SARS-CoV-2. In: Kaiser, M.S., Bandyopadhyay, A., Mahmud, M., Ray, K. (eds) Proceedings of International Conference on Trends in Computational and Cognitive Engineering. Advances in Intelligent Systems and Computing, vol 1309. Springer, Singapore. https://doi.org/10.1007/978-981-33-4673-4_49
Download citation
DOI: https://doi.org/10.1007/978-981-33-4673-4_49
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-4672-7
Online ISBN: 978-981-33-4673-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)