Project Overview
The project is a real-time face mask detection system designed to enhance public health safety by automatically identifying whether individuals are wearing masks. It uses computer vision techniques and a trained deep learning model to detect human faces and classify them as either "mask" or "without mask".
Beyond accurate detection, the system emphasizes efficiency and usability. It can be integrated into surveillance systems, hospital entry points, schools, or workplaces to ensure compliance with mask policies during health emergencies such as COVID-19. The solution is lightweight enough to run on consumer-grade hardware while maintaining high detection accuracy.
Key features include:
- Real-time face detection using OpenCV Haar Cascade classifiers
- Deep learning model trained on labeled mask/no-mask datasets
- Binary classification: "Mask" vs "Without Mask"
- High accuracy with optimized Keras/TensorFlow model
- Live webcam feed integration for continuous monitoring
- Color-coded bounding boxes for clear visualization (green for mask, red for no mask)
- Scalable for integration with CCTV and access control systems
This project highlights the blend of computer vision, machine learning, and practical deployment, offering a real-world solution to public safety monitoring through mask compliance detection.