FACE MASK DETECTION USING ML & DL

The Face Mask Detection Using Machine Learning and Deep Learning project successfully demonstrates the application of artificial intelligence and computer vision for automated face mask monitoring. The proposed system utilizes image processing techniques and a Convolutional Neural Network (CNN) to accurately classify whether a person is wearing a face mask or not. The model provides high detection accuracy and performs efficiently in real-time, making it suitable for practical surveillance applications. Compared with traditional manual monitoring methods, the developed system reduces human effort, minimizes errors, and enables continuous monitoring in crowded public places. The use of deep learning allows the model to automatically learn facial features, resulting in better accuracy under different lighting conditions, face orientations, and backgrounds. The system can be integrated with CCTV cameras and other surveillance devices to improve public safety and ensure compliance with health guidelines. Overall, the proposed face mask detection system is a reliable, fast, and cost-effective solution for real-time mask detection. It has wide applications in hospitals, airports, railway stations, schools, shopping malls, offices, and other public areas. Future improvements may include detecting incorrectly worn masks, recognizing multiple safety equipment such as helmets and face shields, and deploying the system on edge devices and IoT-based smart surveillance platforms for enhanced performance and broader real-world applications.

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