A REVIEW SURVEY: REAL-TIME FACE MASK DETECTION USING CNN AND TRANSFER LEARNING APPROACHES
DOI: 
SHARE THIS ARTICLE
DOWNLOAD ARTICLE
REVIEW REPORT
CITE THIS ARTICLE
In recent times, there has been an alarming rate of spread of airborne diseases like COVID-19. In response, there have been calls for individuals to wear face masks in public places. Manual methods of compliance checking of face masks are not only ineffective but also highly inefficient and prone to errors. This paper discusses an automatic solution for detecting people who wear face masks in public places using convolutional neural network algorithms and transfer learning. Transfer learning helps speed up training time by using previously trained deep learning algorithms. Pre-trained models such as Mobile-Net and Res-Net are used to extract features from images for classification tasks. Using a camera, the algorithm analyzes the video stream, detects people wearing masks, and distinguishes between those without face masks. By applying transfer learning techniques, more accurate predictions are generated using limited training data. Experimental results demonstrate high accuracy and robustness under varying lighting conditions. This system provides a scalable solution for deployment in public environments such as hospitals, airports, and educational institutions
Keywords
Article Information
Article Metrics
HOW TO CITE
Copy
Pandey, A. (2026). A Review Survey: Real-Time Face Mask Detection using CNN and Transfer Learning Approaches. International Journal of Science, Strategic Management and Technology, 02(6). https://doi.org/10.55041/ijsmt.v2i6.102
Pandey, Ankit. "A Review Survey: Real-Time Face Mask Detection using CNN and Transfer Learning Approaches." International Journal of Science, Strategic Management and Technology, vol. 02, no. 6, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i6.102.
Pandey, Ankit. "A Review Survey: Real-Time Face Mask Detection using CNN and Transfer Learning Approaches." International Journal of Science, Strategic Management and Technology 02, no. 6 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i6.102.
References
[2] Y. Zhang, L. Chen, and M. Wang, “Lightweight transfer learning models for edge-based face mask detection,” IEEE Internet of Things Journal, vol. 10, no. 8, pp. 6789–6798, 2023.
[3] A. Alshammari, H. Alsubaie, and M. Alqahtani, “Robust face mask detection under challenging conditions using hybrid deep learning approaches,” IEEE Transactions on Artificial Intelligence, vol. 6, no. 1, pp. 112–123, 2025.
[4] World Health Organization, “Advice on the use of masks in the context of COVID-19,” WHO Guidelines, 2023.
[5] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
[6] P. Kumar, R. Singh, and A. Verma, "Real-time face mask detection using deep learning for safety compliance," IEEE Access, vol. 12, pp. 45678–45689, 2024.
[7] Y. Zhang, L. Chen, and M. Wang, "Lightweight transfer learning models for edge-based face mask detection," IEEE Internet of Things Journal, vol. 10, no. 8, pp. 6789–6798, 2023.
[8] A. Alshammari, H. Alsubaie, and M. Alqahtani, "Robust face mask detection under challenging conditions using hybrid deep learning approaches," IEEE Transactions on Artificial Intelligence, vol. 6, no. 1, pp. 112–123, 2025.
[9] S. Singh and P. Verma, "Comparative analysis of deep learning models for face mask detection," IEEE Conference on Emerging Technologies, pp. 145–150, 2024.
[10] X. Chen, Y. Liu, and Z. Wang, "Efficient real-time face mask detection using model compression techniques," IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 2, pp. 789–801, 2025.
Ethics and Compliance
Indexed In
Similar Articles
Previous Article
