Face Mask Detection and Body Temperature Assessment Using IoT with Deep Learning

Authors

  • Kamali S  UG Scholar, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India
  • Mandalapu Samhita  UG Scholar, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India
  • Srinishi S  UG Scholar, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India
  • Sindhuja P  Assistant Professor, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India

Keywords:

Deep Learning, Mask Detection, IR Sensor, Neural Network, RBM

Abstract

Masks play a crucial role in protecting the health of individuals against respiratory diseases, as is one of the few precautions available for COVID-19 in the absence of immunization. However, some people refuse to wear face masks with so many excuses. Moreover, developing the face mask detector is very crucial in this case. The pre-trained model in the learning approach requires the embedded vision processing system to achieve the accuracy in the detection process. The effective algorithm is required to perform the face wearing level identification in fast manner. The significant objective of the system is to build the automated solution for the detection of the uncovered faces and to create the contactless system to measure the body thermal conditions. The system identifies the level of face mask wearing using the Restricted Boltzmann Machine in Deep learning algorithm. And also identifies the body temperature using the MLX90614 IR Based Contactless sensor. The voice notification is generated using the HMM Model in the audio synthesis circuit.

References

  1. Cappart, Q., Chetelat, D., Khalil, E., Lodi, A., Morris, C. and Velickovic, P., 2021. Combinatorial optimization and reasoning with graph neural networks. arXiv preprint arXiv:2102.09544.
  2. Dong, J., Gong, W., Ming, F. and Wang, L., 2022. A two-stage evolutionary algorithm based on three indicators for constrained multi-objective optimization. Expert Systems with Applications, p.116499.
  3. Fan, C., Zhou, Y. and Tang, Z., 2021. Neighborhood centroid opposite-based learning Harris Hawks optimization for training neural networks. Evolutionary Intelligence, 14(4), pp.1847-1867.
  4. Gaunt, R.E. and Reinert, G., 2021. Bounds for the chi-square approximation of Friedman's statistic by Stein's method. arXiv preprint arXiv:2111.00949.
  5. Irsoy, O. and Alpaydın, E., 2019. Continuously constructive deep neural networks. IEEE transactions on neural networks and learning systems, 31(4), pp.1124-1133.
  6. Islam, M.A., Anderson, D.T., Pinar, A.J., Havens, T.C., Scott, G. and Keller, J.M., 2019. Enabling explainable fusion in deep learning with fuzzy integral neural networks. IEEE Transactions on Fuzzy Systems, 28(7), pp.1291-1300.
  7. Jiang, J. and Fan, J.A., 2021. Multiobjective and categorical global optimization of photonic structures based on ResNet generative neural networks. Nanophotonics, 10(1), pp.361-369.
  8. Kaur, S., Awasthi, L.K. and Sangal, A.L., 2021. HMOSHSSA: a hybrid meta-heuristic approach for solving constrained optimization problems. Engineering with Computers, 37(4), pp.3167-3203.
  9. Liu, M., Chen, L., Du, X., Jin, L. and Shang, M., 2021. Activated gradients for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems.
  10. Luo, J., Zhou, J. and Jiang, X., 2021. A modification of the imperialist competitive algorithm with hybrid methods for constrained optimization problems. IEEE Access, 9, pp.161745-161760.
  11. Qian, C. and Ye, W., 2021. Accelerating gradient-based topology optimization design with dual-model artificial neural networks. Structural and Multidisciplinary Optimization, 63(4), pp.1687-1707.
  12. Radaideh, M.I. and Shirvan, K., 2021. Rule-based reinforcement learning methodology to inform evolutionary algorithms for constrained optimization of engineering applications. Knowledge-Based Systems, 217, p.106836.
  13. Yang, Y., Liu, J. and Tan, S., 2021. A multi-objective evolutionary algorithm for steady-state constrained multi-objective optimization problems. Applied Soft Computing, 101, p.107042.
  14. J. Deng, J. Guo, N. Xue et al., “Arcface: additive angular margin loss for deep face recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4690–4699, Long Beach, CA, USA, June 2019.
  15. N. Zeng, H. Zhang, B. Song et al., “Facial expression recognition via learning deep sparse autoencoders,” Neurocomputing, vol. 273, pp. 4690–4699, 2018.
  16. Y. Shi, L. I. Guanbin, Q. Cao et al., “Face hallucination by attentive sequence optimization with reinforcement learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019.
  17. P. Viola and M. J. Jones, “Robust real-time face detection,” International Journal of Computer Vision, vol. 57, no. 2, pp. 137–154, 2004.
  18. N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 886– 893, San Diego, CA, USA, June 2005.
  19. Kaihan lin , Huimin zhao , Jujian lv , canyao li, “Face Detection and Segmentation Based on Improved Mask R-CNN”, Hindawi Discrete Dynamics in Nature and Society Volume 2020, Article ID 9242917.

International Journal of Scientific Research in Science, Engineering and Technology

Downloads

Published

2022-04-30

Issue

Volume 9, Issue 2, March-April-2022

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Kamali S, Mandalapu Samhita, Srinishi S, Sindhuja P, " Face Mask Detection and Body Temperature Assessment Using IoT with Deep Learning, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 9, Issue 2, pp.368-375, March-April-2022.