International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
권호 표지
DOI QR코드

DOI QR Code

Face Mask Detection Model Using Convolution Neural Network

  • A. A. Abd El-Aziz (Department of Information Systems and Technology, Faculty of Graduate Studies for Statistical Research, Cairo University) ;
  • Nesrine A. Azim (Department of Information Systems and Technology, Faculty of Graduate Studies for Statistical Research, Cairo University) ;
  • Mahmood A. Mahmood (Department of Information Systems, Faculty of Computer and Information Sciences, Jouf University) ;
  • Hamoud Alshammari (Department of Information Systems, Faculty of Computer and Information Sciences, Jouf University)
  • Received : 2024.10.05
  • Published : 2024.10.30
Download PDF
⟨ Previous Next ⟩

Abstract

Corona Virus is a big threat to humanity. Now, the whole world is struggling to reduce the spread of Corona virus. Wearing masks is one of the practices that help to control the spread of the virus according to the world health organization. However, ensuring all people wear facemask is not an easy task. In this paper, we propose a simple and effective model for real-time monitoring using the convolution neural network to detect whether an individual wears a face mask or not. The model is trained, validated, tested upon two datasets. Corresponding to dataset 1, the accuracy of the model was 95.77% and, it was 94.58% for dataset 2.

Keywords

References

  1. W.H.O., "Coronavirus disease 2019 (covid-19): situation report, 205". 2020. 
  2. "Coronavirus Disease 2019 (COVID-19) - Symptoms", Centers for Disease Control and Prevention, 2020. [Online]. Available: https://www.cdc.gov/coronavirus/2019-ncov/symptomstesting/symptoms.html. 2020 
  3. "Coronavirus - Human Coronavirus Types - CDC", Cdc.gov, 2020. [Online]. Available: https://www.cdc.gov/coronavirus/types.html. 2020. 
  4. W.H.O., "Advice on the use of masks in the context of COVID-19: interim guidance", 2020. 
  5. M. Jiang, X. Fan and H. Yan, "RetinaMask: A Face Mask detector", arXiv.org, 2020. [Online]. Available: https://arxiv.org/abs/2005.03950. 2020. 
  6. B. Suvarnamukhi and M. Seshashayee, "Big Data Concepts and Techniques in Data Processing", International Journal of Computer Sciences and Engineering, vol. 6, no. 10, pp. 712-714, 2018. Available: 10.26438/ijcse/v6i10.712714. 
  7. F. Hohman, M. Kahng, R. Pienta and 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, no. 8, pp. 2674-2693, 1 Aug. 2019, doi: 10.1109/TVCG.2018.2843369. 
  8. C. Kanan and G. Cottrell, "Color-to-Grayscale: Does the Method Matter in Image Recognition?", PLoS ONE, vol. 7, no. 1, p. e29740, 2012. Available: 10.1371/journal.pone.0029740. 
  9. Opencv-python-tutroals.readthedocs.io. 2020. Changing Colorspaces - Opencv-Python Tutorials 1 Documentation. [online] Available at:https://opencv-python-tutroals.readthedocs.io/en/latest/pytutorials/pyimgproc/pycolorspaces/pycolorspaces.html. 2020. 
  10. M. Hashemi, "Enlarging smaller images before inputting into convolutional neural network: zero-padding vs. interpolation", Journal of Big Data, vol. 6, no. 1, 2019. Available: 10.1186/s40537-019-0263-7, 2020. 
  11. S. Ghosh, N. Das and M. Nasipuri, "Reshaping inputs for convolutional neural network: Some common and uncommon methods", Pattern Recognition, vol. 93, pp. 79-94, 2019. Available: 10.1016/j.patcog.2019.04.009. 
  12. R. Yamashita, M. Nishio, R. Do and K. Togashi, "Convolutional neural networks: an overview and application in radiology", Insights into Imaging, vol. 9, no. 4, pp. 611-629, 2018. Available: 10.1007/s13244-18-0639-9. 
  13. "Guide to the Sequential model - Keras Documentation", Faroit.com, 2020. [Online]. Available: https://faroit.com/keras-docs/1.0.1/gettingstarted/sequential-model-guide/. 2020. 
  14. Nwankpa, C., Ijomah, W., Gachagan, A. and Marshall, S., 2020. Activation Functions: Comparison Of Trends In Practice And Research For Deep Learning. [online] arXiv.org. Available at: https://arxiv.org/abs/1811.03378. 2020. 
  15. K. Team, "Keras documentation: MaxPooling2D layer", Keras.io, 2020. [Online]. Available: https://keras.io/api/layers/poolinglayers/maxpooling2d/. 2020. 
  16. "prajnasb/observations", GitHub, 2020. [Online]. Available: https://github.com/prajnasb/observations/tree/master/experiements/data. 2020. 
  17. "Face Mask Detection", Kaggle.com, 2020. [Online]. Available: https://www.kaggle.com/andrewmvd/face-mask-detection. 2020. 
  18. "TensorFlow White Papers", TensorFlow, 2020. [Online]. Available: https://www.tensorflow.org/about/bib. 2020. 
  19. K. Team, "Keras documentation: About Keras", Keras.io, 2020. [Online]. Available: https://keras.io/about. 2020. 
  20. "OpenCV", Opencv.org, 2020. [Online]. Available: https://opencv.org/.2020. 
  21. D. Meena and R. Sharan, "An approach to face detection and recognition," 2016 International Conference on Recent Advances and Innovations in Engineering (ICRAIE), Jaipur, pp. 1-6, 2016, doi: 10.1109/ICRAIE.2016.7939462. 
  22. S. Ge, J. Li, Q. Ye and Z. Luo, "Detecting Masked Faces in the Wild with LLE-CNNs," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, pp. 426-434, 2017, doi: 10.1109/CVPR.2017.53. 
  23. S. Garg, S. Mittal, P. Kumar and V. Anant Athavale, "DeBNet: Multilayer Deep Network for Liveness Detection in Face Recognition System," 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN), pp. 1136-1141, 2020, doi: 10.1109/SPIN48934.2020.9070853. 
  24. M. Geetha, R. S. Latha, S. K. Nivetha, S. Hariprasath, S. Gowtham and C. S. Deepak, "Design of face detection and recognition system to monitor students during online examinations using Machine Learning algorithms," 2021 International Conference on Computer Communication and Informatics (ICCCI), 2021, pp. 1-4, doi: 10.1109/ICCCI50826.2021.9402553. 
  25. L. Pang, Y. Ming and L. Chao, "F-DR Net:Face detection and recognition in One Net," 2018 14th IEEE International Conference on Signal Processing (ICSP), pp. 332-337, 2018, doi: 10.1109/ICSP.2018.8652436.