Face Mask Recognition Using Machine Learning

The COVID-19 pandemic has highlighted the importance of wearing face masks to reduce the spread of airborne diseases. Manual enforcement of mask-wearing is inefficient, leading to the need for automated face mask detection systems. This paper presents a machine learning-based face mask recognition system that detects whether a person is wearing a mask in real-time. The proposed system leverages deep learning models such as Convolutional Neural Networks (CNNs) and MobileNetV2 for high-accuracy classification. The dataset comprises images of masked and unmasked faces, preprocessed for model training. Experimental results demonstrate that the proposed model achieves over 98% accuracy in face mask recognition, making it suitable for deployment in public surveillance systems, workplaces, and smart entry systems.