Abstract
The COVID-19 pandemic has necessitated the widespread use of masks, and in India, mask-wearing in public gatherings has become mandatory, with violators being fined. In densely populated nations like India, strict regulations must be established and enforced to mitigate the pandemic’s impact. Authorities and cameras conduct real-time monitoring of individuals leaving their homes, but 24/7 surveillance by humans is not feasible. A suggested approach to resolve this problem is to connect human intelligence and Artificial Intelligence (AI) by employing two Machine Learning (ML) models to recognize people who aren’t wearing masks in live-stream feeds from surveillance, street, and new IP mask recognition cameras. The effectiveness of this method has been demonstrated through its high accuracy compared to other algorithms. The first ML model uses the YOLO (You Only Look Once) model to recognize human faces in real-time video streams. The second ML model is a pre-trained classifier using 180,000 photos to categorize photos of humans into two groups: masked and unmasked. Double is a model that combines face recognition and mask classification into a single model. CNN provides a potential solution that may be utilized with image or video-capturing equipment such as CCTV cameras to monitor security breaches, encourage mask usage, and promote a secure workplace. This study’s proposed mask detection technology utilized pre-trained datasets, face detection, and various classifiers to classify faces as having a proper mask, an improper mask, or no mask. The Double CNN-based model incorporated dual convolutional neural networks and a technology-based warning system to provide real-time facial identification detection. The ML model achieved high performance and accuracy of 98.15%, with the highest precision and recall, and can be used worldwide due to its cost-effectiveness. Overall, the proposed mask detection approach can potentially be a valuable instrument for preventing the spread of infectious diseases.
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References
Thamaraiselvi, D., Mary, S.: Attack and anomaly detection in IoT networks using machine learning. Int. J. Comput. Sci. Mob. Comput.Comput. Sci. Mob. Comput. 9, 95–103 (2020)
Vishwa Kiran, S., Kaur, I., Thangaraj, K., Saveetha, V., Kingsy Grace, R., Arulkumar, N.: Machine learning with data science-enabled lung cancer diagnosis and classification using computed tomography images. Int. J. Image Graph. 23(03), 2240002 (2021)
Ayoobkhan, M.U.A., Yuvaraj, D., Jayanthiladevi, A., Easwaran, B., ThamaraiSelvi, R.: Smart connected digital products and IoT platform with the digital twin. In: Research Advancements in Smart Technology, Optimization, and Renewable Energy, pp. 330–350. IGI Global (2021)
Neelakandan, S., Beulah, J.R., Prathiba, L., Murthy, G.L.N., Irudaya Raj, E.F., Arulkumar, N.: Blockchain with deep learning-enabled secure healthcare data transmission and diagnostic model. Int. J. Model. Simul. Sci. Comput. 13(04), 2241006 (2022)
Thamarai Selvi, R., George Dharma Prakash Raj, E.: Information retrieval models: a survey. Int. J. Res. Rev. Inf. Sci. (IJRRIS) 2(3), 227–233 (2012)
Thamarai Selvi, R., George Dharma Prakash Raj, E.: An approach to improve precision and recall for ad-hoc information retrieval using SBIR algorithm. In: 2014 World Congress on Computing and Communication Technologies, pp. 137–141. IEEE (2014)
Ejaz, M.S., Islam, M.R., Sifatullah, M., Sarker, A.: Implementation of principal component analysis on masked and non-masked face recognition. In: 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), pp. 1–5 (2019)
Qin, B., Li, D.: Identifying facemask-wearing condition using image super-resolution with classification network to prevent COVID-19. Sensors 20(18), 5236 (2020)
Singh, S., Ahuja, U., Kumar, M., Kumar, K., Sachdeva, M.: Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment. Multimedia Tools Appl. 80, 19753–19768 (2021)
Kaur, G., et al.: Face mask recognition system using CNN model. Neurosci. Inform. 2(3), 100035 (2022)
Chavda, A., Dsouza, J., Badgujar, S., Damani, A.: Multi-stage CNN architecture for face mask detection. In: 2021 6th International Conference for Convergence in Technology (i2ct), pp. 1–8. IEEE (2021)
Meivel, S., et al.: Mask detection and social distance identification using Internet of Things and faster R-CNN algorithm. Comput. Intell. Neurosci. 2022 (2022)
Zhu, Z., Cheng, Y.: Application of attitude tracking algorithm for face recognition based on OpenCV in the intelligent door lock. Comput. Commun.. Commun. 154, 390–397 (2020)
Mbunge, E., Simelane, S., Fashoto, S.G., Akinnuwesi, B., Metfula, A.S.: Application of deep learning and machine learning models to detect COVID 19 face masks-a review. Sustain. Oper. Comput. 2, 235–245 (2021)
Khan, A.A., Yadav, S., Kumar, L.: Face mask detection using OpenCv and machine learning. In: 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), pp. 80–85. IEEE (2022)
Thamaraiselvi, R., Mary, S.A.S.: A survey of machine learning for IoT networks (2020)
Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, pp. 4396–4405 (2019). https://doi.org/10.1109/CVPR.2019.00453. Larxel, Face Mask Detection, Kaggle - Version 1, May 2020. https://www.kaggle.com/andrewmvd/face-maskdetection
Thamarai Selvi, R.: An ensemble neural network technique for improving security among various domains of information technology. ICTACT J. Commun. Technol. 14(1), 2882–2888 (2023)
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Thamarai Selvi, R., Arulkumar, N., Ramasamy, G. (2024). EFMD-DCNN: Efficient Face Mask Detection Model in Street Camera Using Double CNN. In: Aurelia, S., J., C., Immanuel, A., Mani, J., Padmanabha, V. (eds) Computational Sciences and Sustainable Technologies. ICCSST 2023. Communications in Computer and Information Science, vol 1973. Springer, Cham. https://doi.org/10.1007/978-3-031-50993-3_34
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