Real-Time Face Mask Detection using Deep Learning

Pranad  Munjal; Vikas  Rattan; Rajat Dua; Varun  Malik

doi:10.15415/jtmge.2021.121003

Authors

Pranad Munjal Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab-140401, India https://orcid.org/0000-0003-0819-2281
Vikas Rattan Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab-140401, India
Rajat Dua Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab-140401, India
Varun Malik Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab-140401, India

DOI:

https://doi.org/10.15415/jtmge.2021.121003

Keywords:

COVID-19, Mask, Machine Learning, CNN, Deep learning, Computer vision

Abstract

The outbreak of COVID-19 has taught everyone the importance of face masks in their lives. SARS-COV-2(Severe Acute Respiratory Syndrome) is a communicable virus that is transmitted from a person while speaking, sneezing in the form of respiratory droplets. It spreads by touching an infected surface or by being in contact with an infected person. Healthcare officials from the World Health Organization and local authorities are propelling people to wear face masks as it is one of the comprehensive strategies to overcome the transmission. Amid the advancement of technology, deep learning and computer vision have proved to be an effective way in recognition through image processing. This system is a real-time application to detect people if they are wearing a mask or are without a mask. It has been trained with the dataset that contains around 4000 images using 224x224 as width and height of the image and have achieved an accuracy rate of 98%. In this research, this model has been trained and compiled with 2 CNN for differentiating accuracy to choose the best for this type of model.It can be put into action in public areas such as airports, railways, schools, offices, etc. to check if COVID-19 guidelines are being adhered to or not.

Downloads

Download data is not yet available.

References

Bengio, Y., Courville, A., & Vincent, P. (2013). Representation Learning: A Review and New Perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798-1828. https://doi.org/10.1109/TPAMI.2013.50

Boyko, N., Basystiuk, O., & Shakhovska, N. (2018). Performance Evaluation and Comparison of Software for Face Recognition, Based on Dlib and Opencv Library. 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine. https://doi.org/10.1109/DSMP.2018.8478556

Chakraborty, A. (2020). Face Mask Detection Data. Retrieved from: https://www.kaggle.com/aneerbanchakraborty/face-mask-detection-data/activity

Chavez, S., Long, B., Koyfman, A., & Liang, S. Y.(2020). Coronavirus Disease (COVID-19): A primer for emergency physicians. The American Journal of Emergency Medicine, 44, 220-229. https://doi.org/10.1016/j.ajem.2020.03.036

Chen, Y., Hu, M., Hua, C., Zhai, G., Zhang, J., Li, Q., & Yang, S. X. (2020). Face Mask Assistant: Detection of Face Mask Service Stage Based on Mobile Phone. Preprint arXiv: 2010.06421. https://arxiv.org/abs/2010.06421

Cowling, B. J., et al., (2009). Facemasks and Hand Hygiene to Prevent Influenza Transmission in Households: A Cluster Randomized Trial. Annals of Internal Medicine, 151(7), 437-446. https://doi:10.7326/0003-4819-151-7-200910060-00142

Das, A., Ansari, M. W., & Basak, R. (2020). Covid-19 Face Mask Detection Using TensorFlow, Keras and OpenCV. 2020 IEEE 17th India Council International Conference (INDICON), New Delhi, India. https://doi.org/10.1109/INDICON49873.2020.9342585

Feng, S., Shen, C., Xia, N., Song, W., Fan, M., & Cowling, B. J. (2020). Rational use of face masks in the COVID-19 pandemic. The Lancet Respiratory Medicine, 8(5), 434–436. https://doi.org/10.1016/S2213-2600(20)30134-X

Guillermo, M., Pascua, A. R. A., Billones, R. K., Sybingco, E., Fillone, A., & Dadios, E. (2020). COVID-19 Risk Assessment through Multiple Face Mask Detection using MobileNetV2 DNN. The 9th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2020), Beijing, China. https://isciia2020.bit.edu.cn/docs/20201114082420135149.pdf

Kalas, M. S. (2019). Real Time Face Detection and Tracking using OpenCV. International Journal of Soft Computing and Artificial Intelligence, 2(1), 41-44.

LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceeding of IEEE, 86(11), 2278–2324. https://doi.org/10.1109/5.726791

Lin, K., Zhao, H., Lv, J., Li, C., Liu, X., Chen, R., & Zhao, R. (2020). Face Detection and Segmentation Based on Improved Mask R-CNN. Discrete Dynamics in Nature and Society, 2020, 9242917. https://doi.org/10.1155/2020/9242917

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. https://doi.org/10.1016/j.measurement.2020.108288

Loey, M., Manogaran, G., Taha, M. H. N., & Khalifa, N. E. M. (2021a). Fighting against COVID-19: A novel deep learning model based on YOLO-v2 with ResNet-50 for medical face mask detection. Sustainable Cities and Society, 65, 102600. https://doi.org/10.1016/j.scs.2020.102600

Militante, S. (2019). Fruit Grading of Garcinia Binucao (Batuan) using Image Processing. International Journal of Recent Technology and Engineering (IJRTE), 8(2), 1829-1832.

Militante, S. V., & Dionisio, N. V. (2020). Real-Time Face Mask Recognition with Alarm System using Deep Learning. 2020 11th IEEE Control and System Graduate Research Colloquium (ICSGRC), Shah Alam, Malaysia. https://doi.org/10.1109/ICSGRC49013.2020.9232610

Mohan, P., Paul, A. J., & Chirania, A. (2021). A Tiny CNN Architecture for Medical Face Mask Detection for Resource-Constrained Endpoints. In: Mekhilef, S., Favorskaya, M., Pandey, R. K., Shaw, R. N. (eds.) Innovations in Electrical and Electronic Engineering. Lecture Notes in Electrical Engineering, vol 756. Springer, Singapore. https://doi.org/10.1007/978-981-16-0749-3_52

Pandiyan, P. (2020, December 17). Social Distance Monitoring and Face Mask Detection Using Deep Neural Network. Retrieved from: https://www.researchgate.net/publication/347439579_Social_Distance_Monitoring_and_Face_Mask_Detection_Using_Deep_Neural_Network

Salihbasic, A., & Orehovacki, T. (2019). Development of Android Application for Gender, Age and Face Recognition Using OpenCV. 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia. https://doi.org/10.23919/MIPRO.2019.8756700

Sandler, M., Howard,A., Zhu, M., Zhmoginov, A.,& Liang-Chieh, C. (2018). MobileNetV2: Inverted Residues and Linear Bottlenecks. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA (pp. 4510-4520). https://doi.org/10.1109/CVPR.2018.00474

Yu, P., Zhu, J., Zhang, Z., & Han, Y. (2020). A Familial Cluster of Infection Associated With the 2019 Novel Coronavirus Indicating Possible Person-to-Person Transmission During the Incubation Period. The Journal of Infectious Diseases, 221(11), 1757-1761. https://doi.org/10.1093/infdis/jiaa077