Detection of Improperly Worn Face Masks using Deep Learning – A Preventive Measure Against the Spread of COVID-19.
DOI:
https://doi.org/10.9781/ijimai.2021.09.003Keywords:
Coronavirus COVID-19, Mask Classification, MobileNetV2, OpenCV, Transfer LearningAbstract
Coronavirus disease 2019 has had a pressing impact on people all around the world. Ceasing the spread of this infectious disease is the urgent need of the hour. A vital method of protection against the virus is wearing masks in public areas. Not merely wearing masks but wearing masks properly can ensure that the respiratory droplets do not get transmitted to other people. In this paper, we have proposed a deep learning-based model, which can be used to detect people who are not wearing their face masks properly. A convolutional neural network model based on the concept of transfer learning is trained on a self-made dataset of images and implemented with light-weighted neural network called MobileNetV2 for mobile architectures. OpenCV is used with Caffe framework to detect faces in an input frame which are further forwarded to our trained convolutional neural network for classification. The method has been implemented on various input images and classification results have been obtained for the same. The experimental results show that the proposed model achieves a testing accuracy and training accuracy of 93.58% and 92.27% respectively. Optimal results with high confidence scores and correct classification have also been achieved when the proposed model was tested on individual input images.
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V. C.-Ch. Cheng et al., “The role of community-wide wearing of face mask for control of coronavirus disease 2019 (COVID-19) epidemic due to SARS-CoV-2,” Journal of Infection, vol. 81, no. 1, pp. 107–114, 2020, doi: 10.1016/j.jinf.2020.04.024.
A. N. Desai and D. M. Aronoff, “Masks and Coronavirus Disease 2019 (COVID-19),” JAMA, vol. 323, no. 20, p. 2103, May 2020, doi: 10.1001/jama.2020.6437.
C. Kenyon, “The prominence of asymptomatic superspreaders in transmission mean universal face masking should be part of COVID-19 de-escalation strategies,” International Journal of Infectious Diseases, vol. 97, pp. 21–22, 2020.
N. H. L. Leung et al., “Respiratory virus shedding in exhaled breath and efficacy of face masks,” Nature Medicine, vol. 26, no. 5, pp. 676–680, 2020, doi: 10.1038/s41591-020-0843-2.
Y. Liu, A. A. Gayle, A. Wilder-Smith, and J. Rocklöv, “The reproductive number of COVID-19 is higher compared to SARS coronavirus,” Journal of Travel Medicine, vol. 27, no. 2, Feb. 2020, doi: 10.1093/jtm/taaa021.
M. P. Fang, Yaqing, Yiting Nie, “Transmission dynamics of the COVID‐19 outbreak and effectiveness of government interventions: A data‐driven analysis,” Journal of medical virology, vol. 92, no. 6, pp. 645–659, 2020, doi: 10.1002/jmv.25750.
E. S. K. Lim, Soo, Ho Il Yoon, Kyoung-Ho Song and H. Bin Kim, “Face Masks and Containment of Coronavirus Disease 2019 (COVID-19): Experience from South Korea,” The Journal of Hospital Infection, 2020, doi: 10.1016/j.jhin.2020.06.017.
S. Esposito, N. Principi, C. C. Leung, and G. B. Migliori, “Universal use of face masks for success against COVID-19: evidence and implications for prevention policies,” European Respiratory Journal, p. 2001260, Jan. 2020, doi: 10.1183/13993003.01260-2020.
M. Dhalaria and E. Gandotra, “Convolutional Neural Network for Classification of Android Applications Represented as Grayscale Images,” International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 12S, pp. 2278–3075, 2019.
H. Panwar, P. K. Gupta, M. K. Siddiqui, R. Morales-Menendez, and V. Singh, “Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet,” Chaos, Solitons & Fractals, vol. 138, p. 109944, 2020, doi: 10.1016/j.chaos.2020.109944.
F. Sultana, A. Sufian, and P. Dutta, “Advancements in Image Classification using Convolutional Neural Network,” in 2018 Fourth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), 2018, pp. 122–129, doi: 10.1109/ICRCICN.2018.8718718.
H. Shin et al., “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1285–1298, 2016, doi: 10.1109/TMI.2016.2528162.
M. Coşkun, A. Uçar, Ö. Yildirim, and Y. Demir, “Face recognition based on convolutional neural network,” in 2017 International Conference on Modern Electrical and Energy Systems (MEES), 2017, pp. 376–379, doi: 10.1109/MEES.2017.8248937.
Szegedy et al., “Going deeper with convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1–9.
Wang et al., “Residual attention network for image classification,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 3156–3164.
Z. Zhao, P. Zheng, S. Xu, and X. Wu, “Object Detection With Deep Learning: A Review,” IEEE Transaction on neural networks and learning systems, vol. 30, no. 11, pp. 3212–3232, 2019, doi: 10.1109/TNNLS.2018.2876865.
Redmon, Joseph, S. Divvala, R. Girshik, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 779–788.
A. Kumar, A. Kaur, and M. Kumar, “Face detection techniques: a review,” Artificial Intelligence Review, Springer, vol. 52, no. 2, pp. 927–948, 2019, doi: 10.1007/s10462-018-9650-2.
A. S. M. and V. M. M. Sivaram, V. Porkodi, “Detection of Accurate Facial Detection using Hybrid Deep Convolutional Recurrent Neural Network,” ICTACT Journal of Soft Computing, vol. 9, no. 2, 2019, doi: 10.21917/ijsc.2019.0256.
M. Khan, S. Chakraborty, R. Astya, and S. Khepra, “Face Detection and Recognition Using OpenCV,” in 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), 2019, pp. 116–119, doi: 10.1109/ICCCIS48478.2019.8974493.
T. Meenpal, A. Balakrishnan, and A. Verma, “Facial Mask Detection using Semantic Segmentation,” in 2019 4th International Conference on Computing, Communications and Security (ICCCS), 2019, pp. 1–5, doi: 10.1109/CCCS.2019.8888092.
M. Jiang, X. Fan, and H. Yan, “RetinaMask: A Face Mask detector.” 2020, [Online]. Available: http://arxiv.org/abs/2005.03950
T. Zhang, J. Li, W. Jia, J. Sun, and H. Yang, “Fast and robust occluded face detection in ATM surveillance,” Pattern Recognition Letters, vol. 107, pp. 33–40, 2018, doi: 10.1016/j.patrec.2017.09.011.
H. Baojin, “Real-World Masked Face Dataset, RMFD.” Real-World Masked Face Dataset, RMFD.
“Mobilenets: Efficient convolutional neural networks for mobile vision applications,” 2017, [Online]. Available: https://arxiv.org/abs/1704.04861
J. Deng, W. Dong, R. Socher, L. Li, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 248–255, doi: 10.1109/CVPR.2009.5206848.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 4510–4520. [28] “How to train a Face Detector,” https://github.com/opencv/opencv/blob/4.0.0-beta/samples/dnn/face_detector/how_to_train_face_detector.txt
“deploy.prototxt,” https://github.com/opencv/opencv/blob/4.0.0-beta/samples/dnn/face_detector/deploy.prototxt
Y. Jia et al., “Caffe: Convolutional Architecture for Fast Feature Embedding,” in Proceedings of the 22nd ACM International Conference on Multimedia, 2014, pp. 675–678, doi: 10.1145/2647868.2654889.
D. L. Streiner and J. Cairney, “What’s under the ROC? An Introduction to Receiver Operating Characteristics Curves,” The Canadian Journal of Psychiatry, vol. 52, no. 2, pp. 121–128, Feb. 2007, doi: 10.1177/070674370705200210.
Yeh and Shi-Tao, “Using trapezoidal rule for the area under a curve calculation,” Proceedings of the 27th Annual SAS User Group International 2002.
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