An Automated System for Facial Mask Detection and Social Distancing during COVID-19 Pandemic
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-16-9447-9_3
Reference15 articles.
1. M.M. Rahman, M.M.H. Manik, M.M. Islam, S. Mahmud, J.-H. Kim, 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), pp. 1–5 (2020). https://doi.org/10.1109/IEMTRONICS51293.2020.9216386
2. A. Chavda, J. Dsouza, S. Badgujar, A. Damani, Multi-stage CNN architecture for face mask detection, in 2021 6th International Conference for Convergence in Technology (I2CT), pp. 1–8 (2021). https://doi.org/10.1109/I2CT51068.2021.9418207
3. M. Jiang, X. Fan, H. Yan, RetinaMask: a face mask detector (2020). [Online]. Available: http://arxiv.org/abs/2005.03950
4. M.S. Ejaz, M.R. Islam, M. Sifatullah, A. Sarker, Implementation of principal component analysis on masked and non-masked face recognition, in 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT) (Dhaka, Bangladesh, 2019), pp. 1–5. https://doi.org/10.1109/ICASERT.2019.8934543
5. S. Ge, J. Li, Q. Ye, Z. Luo, Detecting masked faces in the wild with LLE-CNNs, in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Honolulu, HI, 2017), pp. 426–434. https://doi.org/10.1109/CVPR.2017.53
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