Abstract :

The Rapid Spread Of Airborne Diseases Has Highlighted The Critical Need For Effective Monitoring Of Mask Compliance In Public Spaces. MaskGuard: Intelligent Face Mask Detection System Presents A Robust And Automated Solution That Leverages Machine Learning And Computer Vision To Accurately Identify Individuals Wearing Or Not Wearing Face Masks In Real Time. The System Utilizes A Deep Convolutional Neural Network (CNN) Trained On Diverse Datasets To Achieve High Detection Accuracy Under Varying Lighting Conditions, Facial Orientations, And Occlusions. Integrated With Live Video Surveillance, MaskGuard Performs Continuous Monitoring And Instantly Flags Mask Violations, Enabling Faster Responses And Improved Safety Enforcement. This Work Demonstrates The Capability Of ML-driven Solutions To Enhance Public Health Measures By Providing A Scalable, Efficient, And Reliable Framework For Mask Detection. The Proposed System Can Also Be Extended To Edge Devices For Low-latency Monitoring In Densely Populated Environments. Keywords:Face Mask Detection, Computer Vision, Convolutional Neural Networks (CNNs), Machine Learning, Real-time Surveillance, Public Health Monitoring, Mask Compliance, Deep Learning, Edge Computing, Automated Safety Systems.

Published:

11-12-2025

Issue:

Vol. 25 No. 12 (2025)

Page Nos:

95-101

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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