Abstract
COVID-19 is a life-threatening virus which affected people at a global level in just a matter of few months and is highly contagious. In order to reduce its spread, SOPs must be followed, such as washing hands, wearing face masks, and maintaining social distance. Hence, to aid the strict follow up of SOPs, this paper proposes a system to detect whether the people are wearing face masks and maintaining social distance or not in order to break the chain of COVID 19. The proposed system uses Deep Learning (DL) model based on Convolutional Neural Network (CNN) architecture for training the facemask detector and OpenPose 2D skeleton extraction technique for detecting social distance. A DL model based on a 7-layered CNN architecture was proposed in this research to detect masked and unmasked faces. Based on the proposed technique, 99.98% validation and 99.98% testing accuracies were achieved. In addition to that, the maintenance of social distance which is the new normal nowadays was also detected using the images obtained from the internet as currently, there is no such database available for detecting social distancing.
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References
Akbar, J., et al.: Runway detection and localization in aerial images using deep learning. In: 2019 Digital Image Computing: Techniques and Applications, DICTA 2019, Institute of Electrical and Electronics Engineers Inc. (2019)
Alsaeedy, A.A.R., Chong, E.K.P.: Detecting regions at risk for spreading COVID-19 using existing cellular wireless network functionalities. IEEE Open J. Eng. Med. Biol. 1, 187–189 (2020)
Begum, A., Fatima, F., Sabahath, A.: Implementation of deep learning algorithm with perceptron using tenzorflow library. In: Proceedings of the 2019 IEEE International Conference on Communication and Signal Processing, ICCSP 2019, Institute of Electrical and Electronics Engineers Inc., pp. 172–175 (2019)
Betina, V.: (Improved PPF)going further with point pair features. Neoplasma 16(1), 23–32 (2016). https://doi.org/10.1007/978-3-319-46487-9
Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Realtime multi-person 2D pose estimation using part affinity fields. In: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, pp. 1302–1310 (Jan 2017)
Dutta, S., Manideep, B.C.S., Rai, S., Vijayarajan, V.: A comparative study of deep learning models for medical image classification. IOP Conf. Ser. Mater. Sci. Eng. 263(4), 9 (2017)
Ge, S., Li, J., Ye, Q., Luo, Z.: 2017-January Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 Detecting Masked Faces in the Wild with LLE-CNNs (2017)
Kamiş, S., Goularas, D.: Evaluation of deep learning techniques in sentiment analysis from twitter data. In: Proceedings - 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications, Deep-ML 2019, Institute of Electrical and Electronics Engineers Inc., pp. 12–17 (2019)
Li, H., et al.: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition A Convolutional Neural Network Cascade for Face Detection, 07–12 June 2015 (2015)
Oh, Y., Park, S., Ye, J.C.: Deep learning COVID-19 features on CXR using limited training data sets. IEEE Trans. Med. Imaging 39(8), 2688–2700 (2020)
Sargano, A.B., Angelov, P., Habib, Z.: A Comprehensive review on handcrafted and learning-based action representation approaches for human activity recognition. Appl. Sci. (Switzerland) 7(1), 1–110 (2017)
Wang, Z., et al.: Masked Face Recognition Dataset and Application (2020). http://arxiv.org/abs/2003.09093
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Malik, N.U.R., Bakar, S.A.R.A., Sheikh, U.U., Airij, A.G. (2022). Convolutional Neural Network Architecture for Detecting Facemask and Social Distancing: A Preventive Measure for COVID19. In: Mahyuddin, N.M., Mat Noor, N.R., Mat Sakim, H.A. (eds) Proceedings of the 11th International Conference on Robotics, Vision, Signal Processing and Power Applications. Lecture Notes in Electrical Engineering, vol 829. Springer, Singapore. https://doi.org/10.1007/978-981-16-8129-5_144
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