Skip to main content

IoT Based Framework for the Detection of Face Mask and Temperature, to Restrict the Spread of Covids

  • Conference paper
  • First Online:
Expert Clouds and Applications

Abstract

Coronavirus is a novel virus that is responsible for causing the disease, COVID-19, which is deadly. It was first detected in December 2019, in the city of Wuhan, China and due to its contagious nature, people all over the wor1d are now infected. COVID spreads very fast and easily through air, from one person to another, affecting almost the entire population of the world in a short span. Wearing a mask in public is very much necessary as a means of preventive measure against the viral disease. Moreover, body temperature is an important factor to be identified to determine whether an individual is affected from the virus. Manually checking if a person wears a mask in outdoors or determining the temperature of an individual in a crowded area, is a tedious task and requires an urgent need for solution. Internet technology introduction into the world is beneficial and it can transmit the data without any human interaction which is best suited for this Covid-19 situation. This article provides the road map to how this technology can be utilized for a better cause. In this work, an IoT based framework is designed to ensure the restriction of entry of a Covid affected individual into the premise, by detecting if the mask is worn and his temperature is normal, to avoid the spread of this disease. Moreover, using this method the safety of the staff in the checking process at the entry point is protected.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. WHO, Coronavirus disease 2019 covid-19. https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200812-covid-19-sitrep-205.pdf?sfvrsn=627c9aa8_2 (2020)

  2. “Coronavirus disease 2019 (COVID-19)—Symptoms”, Centers for disease control and prevention, https://www.cdc.gov/coronavirus/2019-ncov/symptomstesting/symptoms.html. (2020)

  3. Corna virus—Human coronavirus types—CDC, https://www.cdc.gov/coronavirus/types.html (2020)

  4. WHO, Advice on the use of masks in the context of COVID-19: interim guidance (2020)

    Google Scholar 

  5. M. Jiang, X. Fan, H. Yan, RetinaMask: a face mask detector, https://arxiv.org/abs/2005.03950 (2020)

  6. N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), vol. 1. (IEEE, 2005)

    Google Scholar 

  7. S. Ge, J. Li, Q. Ye, Z. Luo, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 2682–2690

    Google Scholar 

  8. S. Ghosh, N. Das, M. Nasipuri, Reshaping inputs for convolutional neural network: Some common and uncommon methods. Pattern Recogn. 93, 79–94 (2019)

    Article  Google Scholar 

  9. prajnasb/observatins, GitHub, https://github.com/prajnasb/observations/tree/master/experiements/data (2020)

  10. S. Bharathi et al., An automatic real-time face mask detection using CNN, in 2021 Smart Technologies, Communication and Robotics (STCR). (IEEE, 2021)

    Google Scholar 

  11. A. Das, M.W. Ansari, R. Basak, Covid-19 face mask detection using TensorFlow, Keras and OpenCV, in 2020 IEEE 17th India Council International Conference (INDICON) (IEEE, 2020).

    Google Scholar 

  12. X. Deng et al., A classification–detection approach of COVID-19 based on chest X-ray and CT by using keras pre-trained deep learning models. Comput. Model. Eng. Sci. 125(2), 579–596 (2020)

    Google Scholar 

  13. A. Dumala, A. Papasani, S. Vikkurty, COVID-19 face mask live detection using OpenCV, in Smart Computing Techniques and Applications (Springer, Singapore, 2021), pp. 347–352

    Google Scholar 

  14. B. Suvarnamukhi, M. Seshashayee, Big data concepts and techniques in data processing. Int. J. Comput. Sci. Eng. 6(10):712–714 (2018). https://doi.org/10.26438/ijcse/v6i10.712714

  15. C. Kanan, G. Cottrell, Color-to-Grayscale https://www.researchgate.net/publication/221755665 (2012)

  16. I.J. Jacob, P.E. Darney, Design of deep learning algorithm for IoT application by image based recognition. J. ISMAC 3(03), 276–290 (2021)

    Google Scholar 

  17. R. Memisevic, Deep learning: Architectures, algorithms, applications, in Conference: 2015 IEEE Hot Chips 27 Symposium (HCS), Aug 2015. https://doi.org/10.1109/HOTCHIPS.2015.7477319

  18. F. Hohman, M. Kahng, R. Pienta, D.H. Chau, Visual analytics in deep learning: An interrogative survey for the next frontiers, in IEEE Transactions on Visualization and Computer Graphics, vol. 25(8), pp. 2674–2693, 1 Aug 2019. https://doi.org/10.1109/TVCG.2018.2843369

  19. H. Yi, S. Shiyu, D. Xiusheng, C. Zhigang, A study on deep neural networks framework, in 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), 2016, pp. 1519–1522. https://doi.org/10.1109/IMCEC.2016.7867471

  20. P. Sharma, A. Singh, Era of deep neural networks: A review, in 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2017, pp. 1–5. https://doi.org/10.1109/ICCCNT.2017.8203938

  21. T.S. Rao, S.A. Devi, P. Dileep, M.S. Ram, A Novel approach to detect face mask to control covid using deep learnig. https://ejmcm.com/article_2807_b1e004fc8cf0f8080144eb4707a0b85a.pdf

  22. R. Yamashita, M. Nishio, R. Do, K. Togashi, Convolutional neural networks: an overview and application in radiology. https://insightsimaging.springeropen.com/articles/10.1007/s13244-018-0639-9 (2018)

  23. V. Balasubramaniam, IoT based biotelemetry for smart health care monitoring system. J. Inf. Technol. Digit. World 2(3), 183–190 (2020)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ramya Srikanteswara .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Srikanteswara, R., Hegde, A.S., Abhishek, K., Sai, R.D., Gnanadeep, M.V. (2022). IoT Based Framework for the Detection of Face Mask and Temperature, to Restrict the Spread of Covids. In: Jacob, I.J., Kolandapalayam Shanmugam, S., Bestak, R. (eds) Expert Clouds and Applications. Lecture Notes in Networks and Systems, vol 444. Springer, Singapore. https://doi.org/10.1007/978-981-19-2500-9_12

Download citation

Publish with us

Policies and ethics