AI-BASED FACE COVERING DETECTION AND ALERT SYSTEM FOR SECURE AND RESTRICTED AREAS
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Department of Computer science Sree Sakthi Engineering College, Tamil nadu, India
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Abstract
In high-security environments, the presence of unauthorized face coverings (masks, helmets, or balaclavas) can pose significant security risks by hindering facial identification. This paper proposes an automated detection and alert system leveraging the YOLO (You Only Look Once) v8 architecture and IoT-enabled notification protocols. We utilized a custom dataset of 5,000+ images to train a model capable of distinguishing between "No Face Covering," "Medical Mask," and "Unauthorized Covering" with a mean Average Precision (mAP) of 97.4%. The system integrates with existing CCTV infrastructure to provide real-time alerts to security personnel via a cloud-based dashboard, ensuring rapid response in restricted zones.
Keywords
Artificial Intelligence Object Detection YOLOv8 Security Systems Face Covering Detection Real-time Surveillance 3. Trigger an automated alert when a security protocol is breached
Article Information
PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 03
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Mar 04 2026
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MARY, K. Y. ,. K. ,. L. ,. M. (2026). AI-Based Face Covering Detection and Alert System for Secure and Restricted Areas. International Journal of Science, Strategic Management and Technology, 02(03). https://doi.org/10.55041/ijsmt.v2i3.010
MARY, KADAPA. "AI-Based Face Covering Detection and Alert System for Secure and Restricted Areas." International Journal of Science, Strategic Management and Technology, vol. 02, no. 03, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i3.010.
MARY, KADAPA. "AI-Based Face Covering Detection and Alert System for Secure and Restricted Areas." International Journal of Science, Strategic Management and Technology 02, no. 03 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i3.010.
References
1.Wang, C. Y., et al. (2023). "YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). (Provides a baseline for why newer YOLO versions were chosen over predecessors).
2.Ge, Z., et al. (2021). "YOLOX: Exceeding YOLO Series in 2021." arXiv preprint. (Discusses anchor-free detection methods which influenced the YOLOv8 architecture used in this system).
3.Li, S., et al. (2022). "A Robust Face Mask Detection Algorithm Based on Improved YOLOv5 for Complex Environments." IEEE Access. (Focuses on environmental challenges like low lighting and occlusion, relevant to secure restricted areas).
4.Zhang, Y., et al. (2024). "Edge Computing-Enabled Real-time Surveillance: A Deep Learning Approach for Anomaly Detection." Journal of Network and Computer Applications. (Supports the implementation of the alert system on edge devices).
5.He, K., et al. (2016). "Deep Residual Learning for Image Recognition." CVPR. (Essential for explaining the backbone architectures used for feature extraction in modern AI detectors).
6.Mittal, P., et al. (2022). "Modified MobileNetV2 for Multi-Class Face Mask Detection and Classification." Multimedia Tools and Applications. (Useful for comparing the YOLOv8 approach against lightweight CNN alternatives).
2.Ge, Z., et al. (2021). "YOLOX: Exceeding YOLO Series in 2021." arXiv preprint. (Discusses anchor-free detection methods which influenced the YOLOv8 architecture used in this system).
3.Li, S., et al. (2022). "A Robust Face Mask Detection Algorithm Based on Improved YOLOv5 for Complex Environments." IEEE Access. (Focuses on environmental challenges like low lighting and occlusion, relevant to secure restricted areas).
4.Zhang, Y., et al. (2024). "Edge Computing-Enabled Real-time Surveillance: A Deep Learning Approach for Anomaly Detection." Journal of Network and Computer Applications. (Supports the implementation of the alert system on edge devices).
5.He, K., et al. (2016). "Deep Residual Learning for Image Recognition." CVPR. (Essential for explaining the backbone architectures used for feature extraction in modern AI detectors).
6.Mittal, P., et al. (2022). "Modified MobileNetV2 for Multi-Class Face Mask Detection and Classification." Multimedia Tools and Applications. (Useful for comparing the YOLOv8 approach against lightweight CNN alternatives).
Ethics and Compliance
✓ All ethical standards met
This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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