Enhancing Public Safety with Real-Time Face Mask Detection Using AI
Monitoring mask compliance in public spaces is a critical safety measure, especially during health crises—but manual enforcement through surveillance footage is labor-intensive and often unreliable.
To automate this process, a Convolutional Neural Network (CNN)-based AI model was developed to detect whether individuals are wearing face masks. Trained on a comprehensive image dataset, the system accurately identifies human faces and determines mask presence in real time.
Seamlessly integrable with live CCTV feeds or recorded video files, the solution supports multi-face detection on a single screen—making it ideal for crowded environments like airports, hospitals, and workplaces. This AI-powered system improves surveillance efficiency, ensures safety compliance, and reduces the need for manual monitoring.
Technology Used
Real-Time Face Mask Detection
Developed a CNN-based model to detect human faces and identify mask usage—supporting compliance and safety monitoring.
Live and Recorded Footage Compatibility
Enabled mask detection from both live CCTV feeds and recorded video files—ensuring flexible deployment across environments.
Multi-Face Detection Capability
Accurately recognized multiple individuals and their mask status within a single frame—enhancing scalability for crowded settings.
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