Skip to main content
Top

2023 | OriginalPaper | Chapter

Face Mask Detection: An Application of Artificial Intelligence

Authors : Poonam Mittal, Ashlesha Gupta, Bhawani Sankar Panigrahi, Ruqqaiya Begum, Sanjay Kumar Sen

Published in: Intelligent Systems and Machine Learning

Publisher: Springer Nature Switzerland

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

COVID-19 has been announced as a new pandemic which has affected almost all the countries of the world. Millions of people have become sick and thousands have died due to the respiratory illness caused by the virus. The virus is known to spread when small droplets from nose or mouth of an infected person gets dissolved in air when he or she coughs or exhales or when a person touches a surface infected with virus. The governments all over the world are working on ways to curb the spread of this virus. Multidisciplinary researchers are working to find the best solutions in their own way. Out of the many solutions wearing surgical facemasks is being one of the best preventive measures to limit the spread of corona virus. These masks support filtration of air and adequate breathability. But the problem is that few people don’t use the masks regularly or occasionally due to various reasons like negligence and discomfort etc. This is one of the main causes of high spread of COVID. So, there is a strong need to detect people without mask at public places and to aware them. There are so many initiatives taken by government in this direction, but all have their limitation in one or the other way. So, there is a strong need of a digital solution to ensure that people comply with the government rules of wearing masks in public place sand to recognize unmasked faces on existing monitoring systems to maintain safety and security. Facial recognition systems were being used to identify faces using technology that includes hardware like video cameras. These systems work by combining AI based pattern recognition system along with biometrics to map facial features from an image and compare it with a database of known faces. This research content is also an initiative in this direction to optimize the results.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference Mangla, M., Sharma, N.: Fuzzy modelling of clinical and epidemiological factors for COVID-19 (2020) Mangla, M., Sharma, N.: Fuzzy modelling of clinical and epidemiological factors for COVID-19 (2020)
2.
go back to reference Du, R.-H., et al.: Predictors of mortality for patients with COVID-19 pneumonia caused by SARS-CoV-2: a prospective cohort study. Eur. Respir. J. 55(5) (2020) Du, R.-H., et al.: Predictors of mortality for patients with COVID-19 pneumonia caused by SARS-CoV-2: a prospective cohort study. Eur. Respir. J. 55(5) (2020)
3.
go back to reference Vincent, J.-L., Taccone, F.S.: Understanding pathways to death in patients with COVID-19. Lancet Respir. Med. 8(5), 430–432 (2020)CrossRef Vincent, J.-L., Taccone, F.S.: Understanding pathways to death in patients with COVID-19. Lancet Respir. Med. 8(5), 430–432 (2020)CrossRef
4.
go back to reference Ignatius, T.S.Y., et al.: Evidence of airborne transmission of the severe acute respiratory syndrome virus. New England J. Med. 350(17), 1731–1739 (2004) Ignatius, T.S.Y., et al.: Evidence of airborne transmission of the severe acute respiratory syndrome virus. New England J. Med. 350(17), 1731–1739 (2004)
5.
go back to reference Tellier, R.: Review of aerosol transmission of influenza a virus. Emerg. Infect. Dis. 12(11), 1657 (2006)CrossRef Tellier, R.: Review of aerosol transmission of influenza a virus. Emerg. Infect. Dis. 12(11), 1657 (2006)CrossRef
6.
go back to reference Kazemi, V., Sullivan, J.: One millisecond face alignment with an ensemble of regression trees. IN: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014) Kazemi, V., Sullivan, J.: One millisecond face alignment with an ensemble of regression trees. IN: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014)
7.
go back to reference Fernández-Delgado, M., et al.: Do we need hundreds of classifiers to solve real world classification problems? J. Mach. Learn. Res. 15(1), 3133–3181 (2014) Fernández-Delgado, M., et al.: Do we need hundreds of classifiers to solve real world classification problems? J. Mach. Learn. Res. 15(1), 3133–3181 (2014)
8.
go back to reference Karpathy, A., et al.: Large-scale video classification with convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014) Karpathy, A., et al.: Large-scale video classification with convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014)
9.
go back to reference He, K., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016) He, K., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
12.
go back to reference Dey, S.K., Howlader, A., Deb, C.: 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. AISC, vol. 1309, pp. 603–613. Springer, Singapore (2021). https://doi.org/10.1007/978-981-33-4673-4_49CrossRef Dey, S.K., Howlader, A., Deb, C.: 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. AISC, vol. 1309, pp. 603–613. Springer, Singapore (2021). https://​doi.​org/​10.​1007/​978-981-33-4673-4_​49CrossRef
13.
go back to reference Venkateswarlu, I.B., Kakarla, J., Prakash, S.: Face mask detection using MobileNet and global pooling block. In: 2020 IEEE 4th Conference on Information & Communication Technology (CICT). IEEE (2020) Venkateswarlu, I.B., Kakarla, J., Prakash, S.: Face mask detection using MobileNet and global pooling block. In: 2020 IEEE 4th Conference on Information & Communication Technology (CICT). IEEE (2020)
15.
go back to reference Li, C., Cao, J., Zhang, X.: Robust deep learning method to detect face masks. In: Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture (2020) Li, C., Cao, J., Zhang, X.: Robust deep learning method to detect face masks. In: Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture (2020)
16.
go back to reference Rahman, M.M., et al.: An automated system to limit COVID-19 using facial mask detection in smart city network. In: 2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS). IEEE (2020) Rahman, M.M., et al.: An automated system to limit COVID-19 using facial mask detection in smart city network. In: 2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS). IEEE (2020)
19.
go back to reference Helaly, R., et al.: Deep convolution neural network implementation for emotion recognition system. In: 2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA). IEEE (2020) Helaly, R., et al.: Deep convolution neural network implementation for emotion recognition system. In: 2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA). IEEE (2020)
20.
go back to reference Mangla, M., Sayyad, A., Mohanty, S.N.: An AI and computer vision-based face mask recognition and detection system. In: 2021 Second International Conference on Secure Cyber Computing and Communication (ICSCCC). Organized by NIT Jalandhar, Punjab, India, 21–23 May 2021. https://ieeexplore.ieee.org/document/9478175 Mangla, M., Sayyad, A., Mohanty, S.N.: An AI and computer vision-based face mask recognition and detection system. In: 2021 Second International Conference on Secure Cyber Computing and Communication (ICSCCC). Organized by NIT Jalandhar, Punjab, India, 21–23 May 2021. https://​ieeexplore.​ieee.​org/​document/​9478175
Metadata
Title
Face Mask Detection: An Application of Artificial Intelligence
Authors
Poonam Mittal
Ashlesha Gupta
Bhawani Sankar Panigrahi
Ruqqaiya Begum
Sanjay Kumar Sen
Copyright Year
2023
DOI
https://doi.org/10.1007/978-3-031-35081-8_16

Premium Partner