Behind the Mask: Detection and Recognition Based-on Deep Learning
Ade Nurhopipah(1*), Irfan Rifai Azziz(2), Jali Suhaman(3)
(1) Department of Informatics, Universitas Amikom Purwokerto, Purwokerto
(2) Department of Informatics, Universitas Amikom Purwokerto, Purwokerto
(3) Department of Informatics, Universitas Amikom Purwokerto, Purwokerto
(*) Corresponding Author
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E. Noyes, J. P. Davis, N. Petrov, K. L. H. Gray, and K. L. Ritchie, “The effect of face masks and sunglasses on identity and expression recognition with super-recognizers and typical observers,” R. Soc. Open Sci., vol. 8, no. 3, pp. 1–18, 2021. https://doi.org/10.1098/rsos.201169
E. Freud, A. Stajduhar, R. S. Rosenbaum, G. Avidan, and T. Ganel, “The COVID-19 pandemic masks the way people perceive faces,” Sci. Rep., vol. 10, no. 22344, pp. 1–8, 2020. https://doi.org/10.1038/s41598-020-78986-9
M. Marini, A. Ansani, F. Paglieri, F. Caruana, and M. Viola, “The impact of facemasks on emotion recognition, trust attribution and re-identification,” Sci. Rep., vol. 11, no. 5577, pp. 1–14, 2021. https://doi.org/10.1038/s41598-021-84806-5
C. Gupta and Nasib Singh Gill, “Coronamask: A Face Mask Detector for Real-Time Data,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 9, no. 4, pp. 5624–5630, 2020. https://doi.org/10.30534/ijatcse/2020/212942020
M. Sujaritha, S. Kabilan, M. Manikandan, and S. N. Kisore, “Real Time Face Mask Identification Using Deep Learning,” J. Phys. Conf. Ser., vol. 1916, no. 012077, pp. 1–10, 2021. https://doi.org/10.1088/1742-6596/1916/1/012077
S. Ge, J. Li, Q. Ye, and Z. Luo, “Detecting Masked Faces in The Wild with LLE-CNNs,” Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017, vol. 2017-Janua, pp. 426–434, 2017. https://doi.org/10.1109/CVPR.2017.53
M. Loey, G. Manogaran, M. H. N. Taha, and N. E. M.Khalifa, “A Hybrid Deep Transfer Learning Model with Machine Learning Methods for Face Mask Detection in The Era of The COVID-19 Pandemic,” Measurement, vol. 167, 2020. https://doi.org/10.1155/2021/4529107
R. Katari, Sreekar Kaza, B. RamyaSree, V. Divyavani, and M. AbubakarJ, “A Comparative Analysis of Variant Deep Learning Models for COVID-19 Protective Face Mask Detection,” Turkish J. Comput. Math. Educ., vol. 12, no. 6, pp. 2841–2848, 2021. https://doi.org/10.17762/turcomat.v12i6.5791
W. Hariri, “Efficient masked face recognition method during the COVID-19 pandemic,” Signal, Image Video Process., 2021. https://doi.org/10.1007/s11760-021-02050-w
A. S. Joshi, S. S. Joshi, G. Kanahasabai, R. Kapil, and S. Gupta, “Deep Learning Framework to Detect Face Masks from Video Footage,” in Proceedings - 2020 12th International Conference on Computational Intelligence and Communication Networks, CICN 2020, 2020, pp. 435–440. https://doi.org/10.1109/CICN49253.2020.9242625
S. Sethi, M. Kathuria, and T. Kaushik, “Face mask detection using deep learning: An approach to reduce risk of Coronavirus spread,” J. Biomed. Inform., vol. 120, no. 103848, 2021. https://doi.org/10.1016/j.jbi.2021.103848
M. S. Ejaz and M. R. Islam, “Masked face recognition using convolutional neural network,” in 2019 International Conference on Sustainable Technologies for Industry 4.0, STI 2019, 2019, no. December 2019. https://doi.org/10.1109/STI47673.2019.9068044
J. S. Talahua, J. Buele, P. Calvopina, and J. Varela-Aldas, “Facial recognition system for people with and without face mask in times of the covid-19 pandemic,” Sustain., vol. 13, no. 12, pp. 1–19, 2021. https://doi.org/10.3390/su13126900
Y. Said, “Pynq-YOLO-Net: An embedded quantized convolutional neural network for face mask detection in COVID-19 pandemic era,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 9, pp. 100–106, 2020. https://doi.org/10.14569/IJACSA.2020.0110912
S. Abbasi, H. Abdi, and A. Ahmadi, “A Face-Mask Detection Approach based on YOLO Applied for a New Collected Dataset,” in 26th International Computer Conference, Computer Society of Iran, CSICC 2021, 2021, pp. 1–6. https://doi.org/10.1109/CSICC52343.2021.9420599
B. Roy, S. Nandy, D. Ghosh, D. Dutta, P. Biswas, and T. Das, “MOXA: A Deep Learning Based Unmanned Approach For Real-Time Monitoring of People Wearing Medical Masks,” Trans. Indian Natl. Acad. Eng., vol. 5, no. 3, pp. 509–518, 2020. https://doi.org/10.1007/s41403-020-00157-z
A. Kumar, A. Kalia, A. Sharma, and M. Kaushal, “A hybrid tiny YOLO v4-SPP module based improved face mask detection vision system,” Journal of Ambient Intelligence and Humanized Computing. 2021. https://doi.org/10.1007/s12652-021-03541-x
J. Yu and W. Zhang, “Face Mask Wearing Detection Algorithm Based on Improved YOLO-v4,” Sensors, vol. 21, no. 3263, 2021. https://doi.org/10.3390/ s21093263
N. M. Aszemi and P. D. D. Dominic, “Hyperparameter Optimization in Convolutional Neural Network using Genetic Algorithms,” Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 6, pp. 269–278, 2019. https://doi.org/10.14569/ijacsa.2019.0100638
S. Loussaief and A. Abdelkrim, “Convolutional Neural Network Hyper-Parameters Optimization based on Genetic Algorithms,” Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 10, pp. 252–266, 2018. https://doi.org/10.14569/IJACSA.2018.091031
H. Choi, “CNN Output Optimization for More Balanced Classification,” Int. J. Fuzzy Log. Intell. Syst., vol. 17, no. 2, pp. 98–106, 2017. https://doi.org/10.5391/IJFIS.2017.17.2.98
Aurélien Géron, “Deep Computer Vision Using Convolutional Neural Network,” in Hands-On Machine Learning with Scikit-Learn, Keras & tensorFlow, 2nd ed., Sebastopol: O’Reilly Media, Inc., 2019, pp. 445–496.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. December, pp. 770–778, 2016. https://doi.org/10.1109/CVPR.2016.90
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, vol. December, pp. 2818–2826. https://doi.org/10.1109/CVPR.2016.308
F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017, vol. 2017-Janua, pp. 1800–1807. https://doi.org/10.1109/CVPR.2017.195
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 779–788, 2016. https://doi.org/10.1021/je00029a022
J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement,” arXiv:1804.02767v1, pp. 1–6, 2018. https://pjreddie.com/media/files/papers/YOLOv3.pdf
Tzutalin, “LabelImg (Git code ).” 2015.
N. Nguyen, T. Do, T. D. Ngo, and D. Le, “An Evaluation of Deep Learning Methods for Small Object Detection,” Hindawi, 2020. https://doi.org/10.1155/2020/3189691
Y. Ding, Z. Li, and D. Yastremsky, “Real-time Face Mask Detection in Video Data,” arXiv:2105.01816, 2021. http://arxiv.org/abs/2105.01816
DOI: https://doi.org/10.22146/ijccs.72075
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