Autonomic Face Mask Detection with Deep Learning: an IoT Application

Víctor Hugo Benitez Baltazar; Jesús Horacio Pacheco Ramírez; Jose Roberto Moreno Ruiz; Cristian Nuñez Gurrola

doi:10.17488/RMIB.42.2.13

Authors

Víctor Hugo Benitez Baltazar Universidad de Sonora, México https://orcid.org/0000-0002-3926-9352
Jesús Horacio Pacheco Ramírez Universidad de Sonora, México
Jose Roberto Moreno Ruiz Universidad de Sonora, México
Cristian Nuñez Gurrola Universidad de Sonora, México https://orcid.org/0000-0002-6120-984X

DOI:

https://doi.org/10.17488/RMIB.42.2.13

Keywords:

Machine learning, COVID - 19, Cyber-Physical Systems, Internet of Things

Abstract

A new and deadly virus known as SARS-CoV-2, which is responsible for the coronavirus disease (COVID-19), is spreading rapidly around the world causing more than 3 million deaths. Hence, there is an urgent need to find new and innovative ways to reduce the likelihood of infection. One of the most common ways of catching the virus is by being in contact with droplets delivered by a sick person. The risk can be reduced by wearing a face mask as suggested by the World Health Organization (WHO), especially in closed environments such as classrooms, hospitals, and supermarkets. However, people hesitate to use a face mask leading to an increase in the risk of spreading the disease, moreover when the face mask is used, sometimes it is worn in the wrong way. In this work, an autonomic face mask detection system with deep learning and powered by the image tracking technique used for the augmented reality development is proposed as a mechanism to request the correct use of face masks to grant access to people to critical areas. To achieve this, a machine learning model based on Convolutional Neural Networks was built on top of an IoT framework to enforce the correct use of the face mask in required areas as it is requested by law in some regions.

Downloads

Download data is not yet available.

References

Stilianakis NI, Drossinos Y. Dynamics of infectious disease transmission by inhalable respiratory droplets. J R Soc Interface [Internet]. 2010;7(50):1355-1366. Available from: https://doi.org/10.1098/rsif.2010.0026

Çelik I, Saatçi E, Eyüboğlu FO. Emerging and reemerging respiratory viral infections up to Covid-19. Turk J Med Sci [Internet]. 2020;50(SI-1):557-562. Available from: https://doi.org/10.3906/sag-2004-126

Lipsitch M, Cohen T, Cooper B, Robins JM, et al. Transmission Dynamics and Control of Severe Acute Respiratory Syndrome. Science [Internet]. 2003;300 (5627):1966-70. Available from: https://doi.org/10.1126/science.1086616

Blackburn RM, Frampton D, Smith CM, Fragaszy EB, et al. Nosocomial transmission of influenza: A retrospective cross‐sectional study using next generation sequencing at a hospital in England (2012‐2014). Influenza Other Respir Viruses [Internet]. 2019;13(6): 556-563. Available from: https://doi.org/10.1111/irv.12679

Mendelson L. Facing Your Face Mask Duties – A List of Statewide Orders. Insight [Internet]; 2021. Available from: https://www.littler.com/publication-press/publication/facing-your-face-mask-duties-list-statewide-orders

World Health Organization. Advice on the use of masks in the context of COVID-19: interim guidance-2. World Health Organization [Internet]; 2020. Available from: https://apps.who.int/iris/handle/10665/332293

Fadinger H, Schymik J. The Costs and Benefits of Home Office during the Covid-19 Pandemic - Evidence from Infections and an Input-Output Model for Germany. COVID Economics [Internet]. 2020;9(1):107-134. Available from: https://doi.org/10.3886/E124902V2

Otoom M, Otoum N, Alzubaidi MA, Etoom Y, et al. An IoT-based framework for early identification and monitoring of COVID-19 cases. Biomed Signal Process Control [Internet]. 2020;62:102149. Available from: https://doi.org/10.1016/j.bspc.2020.102149

Sahlol AT, Yousri D, Ewees AA, Al-Qaness MAA, et al. COVID-19 image classification using deep features and fractional-order marine predators algorithm. Sci Rep [Internet]. 2020;10(1):15364. Available from: https://doi.org/10.1038/s41598-020-71294-2

Khaleghi A, Moin MS. Improved anomaly detection in surveillance videos based on a deep learning method. 2018 8th Conference of AI & Robotics and 10th RoboCup Iranopen International Symposium (IRANOPEN) [Internet]. Qazvin: IEEE; 2018:73-81. Available from: http://doi.org/10.1109/RIOS.2018.8406634

Lee EA, Seshia SA. Introduction to Embedded Systems, A Cyber-Physical Systems Approach [Internet]. Version 1.8. Berckeley: MIT Press; 2011. Available from: https://ptolemy.berkeley.edu/books/leeseshia/releases/LeeSeshia_DigitalV1_08.pdf

Lee EA. Cyber Physical Systems: Design Challenges. 2008 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC) [Internet]. Orlando: IEEE; 2008:363-369. Available from: http://doi.org/10.1109/ISORC.2008.25

LeCun Y, Bengio Y, Hinton G. Deep learning. Nature [Internet]. 2015;521(7553):436-444. Available from: https://doi.org/10.1038/nature14539

Jin KH, McCann MT, Froustey E, Unser M. Deep Convolutional Neural Network for Inverse Problems in Imaging. IEEE Trans Image Process [Internet]. 2017;26(9):4509-4522. Available from: http://doi.org/10.1109/TIP.2017.2713099

Li H, Lin Z, Shen X, Brandt J, et al. A convolutional neural network cascade for face detection. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) [Internet]. Boston: IEEE; 2015:5325-5334. Available from: http://doi.org/10.1109/CVPR.2015.7299170

Zhu W, Ma Y, Zhou Y, Benton M, et al. Deep Learning Based Soft Sensor and Its Application on a Pyrolysis Reactor for Compositions Predictions of Gas Phase Components. Comput Aided Chem Eng [Internet]. 2018;44(1):2245–2250. Available from: http://doi.org/10.1016/b978-0-444-64241-7.50369-4

Chan T, Jia K, Gao S, Lu J, et al. PCANet: A Simple Deep Learning Baseline for Image Classification? IEEE Trans Image Process [Internet]. 2015;24(12):5017-5032. Available from: http://doi.org/10.1109/TIP.2015.2475625

Shorten C, Khoshgoftaar T. A survey on Image Data Augmentation for Deep Learning. J Big Data [Internet]. 2019;6:60. Available from: https://doi.org/10.1186/s40537-019-0197-0

Escamilla-Ambrosio PJ, Rodríguez-Mota A, Aguirre-Anaya E, Acosta-Bermejo R, et al. Distributing Computing in the Internet of Things: Cloud, Fog and Edge Computing Overview. In Maldonado Y, Trujillo L, Schütze O, Riccardi A, et al (eds). Studies in Computational Intelligence [Internet]. Vol 731. Cham: Springer; 2018. Available from: http://doi.org/10.1007%2F978-3-319-64063-1_4

Al-Bahri M, Yankovsky A, Borodin A, Kirichek R. Testbed for Identify IoT-Devices Based on Digital Object Architecture. In: Galinina O, Andreev S, Balandin S, Koucheryavy Y (eds). Internet of Things, Smart Spaces, and Next Generation Networks and Systems. NEW2AN 2018, ruSMART 2018 [Internet]. Vol. 11118. Cham: Springer. 2018. Available from: https://doi.org/10.1007/978-3-030-01168-0_12

Yassein MB, Shatnawi MQ, Aljwarneh S, Al-Hatmi R. Internet of Things: Survey and open issues of MQTT protocol. 2017 International Conference on Engineering & MIS (ICEMIS) [Internet]. Monastir: IEEE; 2017:1-6. Available from: https://doi.org/10.1109/ICEMIS.2017.8273112

Pacheco J, Tunc C, Satam P, Hariri S. Secure and Resilient Cloud Services for Enhanced Living Environments. IEEE Cloud Comput [Internet]. 2016;3(6):44–52. Available from: https://doi.org/10.1109/MCC.2016.129

Gurav O. Face Mask Detection Dataset. Kaggle [Internet]; 2021. Available from: https://www.kaggle.com/omkargurav/face-mask-dataset

Getting Started with Vuforia Engine in Unity | VuforiaLibrary. PTC Inc [Internet]. [cited 2021 May 24]. Available from: https://library.vuforia.com/articles/Training/getting-started-with-vuforia-in-unity.html

Amin D, Govilkar S. Comparative Study of Augmented Reality SDK’s. Int J Comput Sci Appl [Internet]. 2015;5(1):11–26. Available from: https://doi.org/10.5121/ijcsa.2015.5102