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Research Article
Mask Detection & Alert with the Application of Open CV and Mobilenetv2
Rathish Kumar M1
Santhosh K V2
Harish S3
Hari Priya R4
1234 Computer Technology, Bannari Amman Institute of Technology, Tamilnadu, India.
Published Online: March-April 2023
Pages: 248-252
Cite this article
No DOIReferences
1. Mrs. Monali Nitin chaudhari, Ms. Mrinal Deshmukh, Ms. Gayatri Ramrakhiani, Ms. RakshitaParvatikar, ‘’Face detection using
viola-jones algorithm and neural networks’’, IEEE, 978- 1-5386-5257-2/18, 2018.
2. Jie Wang, Zhao Li, ‘’ Research on face recognition based on CNN’’, 2nd International Symposium on Resource Exploration and
Environmental Science IOP Conf. Ser.: Earth Environ. Sci. 170 032110, 2018.
3. SukhadaChokkadi, Sannidhan MS, Sudeepa K B, AbhirBhandary, ‘’A study on various state of the art of the art face recognition
system using deep learning techniques‘’, International Journal of Advanced Trends in Computer Science and Engineering, Vol 8(4),
July- August 2019.
4. Bosheng Qin and Dongxiao Li, ‘’ Identifying facemask-wearing condition using image superresolution with classification network to
prevent covid-19’’, Sensors, Vol. 20, 2020.
5. Vinitha.V and Velantina.V’Covid-19 facemask detection with deep learning and computer vision’’, International Research Journal of
Engineering and Technology (IRJET), Volume: 07 Issue: 08, Aug 2020.
6. Yan, K., Huang, S., Song, Y., Liu, W., & Fan, N. (2017). Face recognition based on convolution neural network. 2017 36th Chinese
Control Conference(CCC). doi:10.23919/chicc.2017.8027997
7. Loey, M., Manogaran, G., Taha, M. H. N., & Khalifa, N. E. M. (2021). A hybrid deep transfer learning model with machine
learning methods for face mask detection in the era of the COVID-19 pandemic. Measurement, 167, 108288.
8. Ge, S., Li, J., Ye, Q., & Luo, Z. (2017). Detecting Masked Faces in the Wild with LLECNNs. 2017 IEEE Conference on Computer
Vision and Pattern Recognition (CVPR), 426–434
9. .Xin Zhang, Thomas Gonnot, JafarSaniie, “Real-Time Face Detection and Recognition in Complex Background”, Journal of Signal
and Information Processing, 2017, 8, 99-112.
10. K. Sunil Manohar Reddy, “Comparison of Various Face Recognition Algorithms”, International Journal of Advanced Research in
Science, Engineering and Technology Vol. 4, Issue 2, February 2017.
11. S. Feng, C. Shen, N. Xia, W. Song, M. Fan, and B. J. Cowling, “Rational use of face masks in the covid19pandemic,”The Lancet
Respiratory Medicine, 2020.
12. Z. Wang, G. Wang, B. Huang, Z. Xiong, Q. Hong, H. Wu, P. Yi, K. Jiang, N. Wang, Y. Peiet al., “Masked facerecognition dataset
and application,”arXiv preprint arXiv:2003.09093, 2020.
13. Z.-Q. Zhao, P. Zheng, S.-t. Xu, and X. Wu, “Object detection with deep learning: A review,”IEEE transactions on neural
networks and learning systems, vol. 30, no. 11, pp. 3212–3232, 2019.
14. A. Kumar, A. Kaur, and M. Kumar, “Face detection techniques: a review, ”Artificial Intelligence Review, vol. 52,no. 2, pp. 927–
948, 2019.
15. D.-H. Lee, K.-L. Chen, K.-H. Liou, C.-L. Liu, and J.-L. Liu, “Deep learning and control algorithms of direct perception for
autonomous driving,2019.
viola-jones algorithm and neural networks’’, IEEE, 978- 1-5386-5257-2/18, 2018.
2. Jie Wang, Zhao Li, ‘’ Research on face recognition based on CNN’’, 2nd International Symposium on Resource Exploration and
Environmental Science IOP Conf. Ser.: Earth Environ. Sci. 170 032110, 2018.
3. SukhadaChokkadi, Sannidhan MS, Sudeepa K B, AbhirBhandary, ‘’A study on various state of the art of the art face recognition
system using deep learning techniques‘’, International Journal of Advanced Trends in Computer Science and Engineering, Vol 8(4),
July- August 2019.
4. Bosheng Qin and Dongxiao Li, ‘’ Identifying facemask-wearing condition using image superresolution with classification network to
prevent covid-19’’, Sensors, Vol. 20, 2020.
5. Vinitha.V and Velantina.V’Covid-19 facemask detection with deep learning and computer vision’’, International Research Journal of
Engineering and Technology (IRJET), Volume: 07 Issue: 08, Aug 2020.
6. Yan, K., Huang, S., Song, Y., Liu, W., & Fan, N. (2017). Face recognition based on convolution neural network. 2017 36th Chinese
Control Conference(CCC). doi:10.23919/chicc.2017.8027997
7. Loey, M., Manogaran, G., Taha, M. H. N., & Khalifa, N. E. M. (2021). A hybrid deep transfer learning model with machine
learning methods for face mask detection in the era of the COVID-19 pandemic. Measurement, 167, 108288.
8. Ge, S., Li, J., Ye, Q., & Luo, Z. (2017). Detecting Masked Faces in the Wild with LLECNNs. 2017 IEEE Conference on Computer
Vision and Pattern Recognition (CVPR), 426–434
9. .Xin Zhang, Thomas Gonnot, JafarSaniie, “Real-Time Face Detection and Recognition in Complex Background”, Journal of Signal
and Information Processing, 2017, 8, 99-112.
10. K. Sunil Manohar Reddy, “Comparison of Various Face Recognition Algorithms”, International Journal of Advanced Research in
Science, Engineering and Technology Vol. 4, Issue 2, February 2017.
11. S. Feng, C. Shen, N. Xia, W. Song, M. Fan, and B. J. Cowling, “Rational use of face masks in the covid19pandemic,”The Lancet
Respiratory Medicine, 2020.
12. Z. Wang, G. Wang, B. Huang, Z. Xiong, Q. Hong, H. Wu, P. Yi, K. Jiang, N. Wang, Y. Peiet al., “Masked facerecognition dataset
and application,”arXiv preprint arXiv:2003.09093, 2020.
13. Z.-Q. Zhao, P. Zheng, S.-t. Xu, and X. Wu, “Object detection with deep learning: A review,”IEEE transactions on neural
networks and learning systems, vol. 30, no. 11, pp. 3212–3232, 2019.
14. A. Kumar, A. Kaur, and M. Kumar, “Face detection techniques: a review, ”Artificial Intelligence Review, vol. 52,no. 2, pp. 927–
948, 2019.
15. D.-H. Lee, K.-L. Chen, K.-H. Liou, C.-L. Liu, and J.-L. Liu, “Deep learning and control algorithms of direct perception for
autonomous driving,2019.
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