Face Mask Detection Using CNN Model and Transfer Learning

Our society has recently undergone a significant pandemic, presenting us with valuable opportunities for learning and growth. This research study presents a deep learning model designed to automatically recognize masks in public settings, addressing concerns related to the global COVID-19 pandemic and potential influenza outbreaks. The model employs convolutional neural networks (CNN) for the purpose of classifying individuals as either wearing masks or not, utilizing real-time facial image analysis. A diverse compilation of facial image datasets, including individuals of different ages, genders, and ethnic backgrounds, was curated, and appropriately labeled. By employing various techniques such as data augmentation, transfer learning, and fine-tuning, significant enhancements were achieved in the performance of the model. The incorporation of the proposed automated detection system into surveillance systems, public transit networks, and open areas presents significant potential for the effective implementation of mask-wearing regulations. This technology enhances public health by aiding in the management of infectious diseases such as COVID-19 and mitigating their associated risks.