Realtime-Based System for Facemask Detection Using PCA, with CNN and COCO Model
DOI:
https://doi.org/10.51519/journalisi.v6i2.759Keywords:
Machine Learning (ML), Deep neural learning (DL), Convolutional Neural Network (CNN), Principal Component Analysis (PCA), FacemaskAbstract
The instant spread of COVID-19 has underscored the need for effective measures such as wearing face masks to control transmission. As a response, facemask detection systems using advanced machine learning techniques have become essential for ensuring compliance and public safety. This research focused on developing a system for detecting facemask usage using a hybridized approach comprising of Convolutional Neural Networks (CNN), Principal Component Analysis (PCA), and the Common Objects in Context (COCO) model. A hybridized detection model is often explored to enhance the precision and efficiency of previous methods that leveraged traditional machine learning or deep learning for the same task. Hence, this system effectively identifies whether individuals are properly wearing masks, not wearing masks at all, or wearing masks improperly from images and real-time video streams using bounding boxes. The results demonstrate that the hybrid approach achieves high accuracy in detecting various facemask conditions across different scenarios. Evaluation metrics such as Average Precision (AP) and Average Recall (AR) indicate the model's robustness, with a reported AP value of 70% and an AR value of 81%, primarily evaluated on larger objects within images. Further evaluations involving different individuals and types of facemasks revealed variability in detection accuracy, highlighting the model's effectiveness and areas for improvement. Nevertheless, the development and deployment of facemask detection systems are crucial for managing public health and ensuring safety in the face of ongoing and future pandemics.
Downloads
References
World Health Organization, “Coronavirus disease 2019 (COVID-19): situation report, 73,” 2020.
C. G. Dwirusman, “The Role and Effectivity of Face Mask in Preventing Transmission of Coronavirus Disease 2019 (COVID-19),” Jurnal Medika Hutama, vol. 2, no. 01, pp. 412-420, Oct. 2020.
D. Kumar, R. Malviya, and P. K. Sharma, “Corona virus: a review of COVID-19,” EJMO, vol. 4, no. 1, pp. 8-25, 2020.
A. Nowrin, S. Afroz, M. S. Rahman, I. Mahmud, and Y. Z. Cho, “Comprehensive review on facemask detection techniques in the context of covid-19,” IEEE Access, vol. 9, pp. 106839-106864, 2021.
A. Nieto-Rodriguez, M. Mucientes, and V. M. Brea, “System for medical mask detection in the operating room through facial attributes,” in Pattern Recognition and Image Analysis: 7th Iberian Conference, IbPRIA 2015, Santiago de Compostela, Spain, June 17-19, 2015, Proceedings 7, Springer International Publishing, pp. 138-145.
M. S. Ejaz, M. R. Islam, M. Sifatullah, and 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), 2019, pp. 1-5.
B. Qin and D. Li, “Identifying facemask-wearing condition using image super-resolution with classification network to prevent COVID-19,” Sensors, vol. 20, no. 18, p. 5236, 2020.
M. Jiang and X. Fan, “Retinamask: A face mask detector,” arXiv:2005.03950, 2020.
B. Batagelj, P. Peer, V. Štruc, and S. Dobrišek, “How to correctly detect face-masks for covid-19 from visual information?,” Applied Sciences, vol. 11, no. 5, p. 2070, 2021.
M. Loey, G. Manogaran, M. H. N. Taha, and N. E. M. Khalifa, “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, vol. 65, p. 102600, 2021.
P. Nagrath et al., “SSDMNV2: A real time DNN-based face mask detection system using single shot multibox detector and MobileNetV2,” Sustainable Cities and Society, vol. 66, p. 102692, 2021.
N. C. Ristea and R. T. Ionescu, “Are you wearing a mask? Improving mask detection from speech using augmentation by cycle-consistent GANs,” arXiv:2006.10147, 2020.
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, p. 108288, 2021.
H. M. Al-Sarrar and H. H. Al-Baity, “A novel hybrid face mask detection approach using Transformer and convolutional neural network models,” PeerJ Computer Science, vol. 9, p. e1265, 2023.
P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol. 1, pp. I-511, 2001.
T. Y. Lin et al., “Microsoft COCO: Common objects in context,” in Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, Springer International Publishing, pp. 740-755.
Olmsted Medical Center, “Wearing A Mask,” 2024. [Online]. Available: https://www.olmmed.org/covid-19-information/how-to-wear-a-mask/.
R. Padilla, S. L. Netto, and E. A. Da Silva, “A survey on performance metrics for object-detection algorithms,” in 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), 2020, pp. 237-242.
I. Umoren, S. Inyang, & A. Silas, "Intelligent Surveillance and Facial Recognition System for Efficient Border Monitoring and Threats Prediction Using Machine Learning Approach," Researchers Journal of Science and Technology, vol. 1, no. 1, pp. 47-63.
I. Omoronyia, U. Etuk, and P. Inglis, “A privacy awareness system for software design,” International Journal of Software Engineering and Knowledge Engineering, vol. 29, no. 10, pp. 1557-1604, 2019.
Downloads
Published
Issue
Section
License
Authors Declaration
- The Authors certify that they have read, understood, and agreed to the Journal of Information Systems and Informatics (JournalISI) submission guidelines, policies, and submission declaration. The submission has been prepared using the provided template.
- The Authors certify that all authors have approved the publication of this manuscript and that there is no conflict of interest.
- The Authors confirm that the manuscript is their original work, has not received prior publication, is not under consideration for publication elsewhere, and has not been previously published.
- The Authors confirm that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission.
- The Authors confirm that the manuscript is not copied from or plagiarized from any other published work.
- The Authors declare that the manuscript will not be submitted for publication in any other journal or magazine until a decision is made by the journal editors.
- If the manuscript is finally accepted for publication, the Authors confirm that they will either proceed with publication immediately or withdraw the manuscript in accordance with the journal’s withdrawal policies.
- The Authors agree that, upon publication of the manuscript in this journal, they transfer copyright or assign exclusive rights to the publisher, including commercial rights
