Protective Face Mask Recognition using Deep Learning Techniques

The adoption of face mask stands as a pivotal preventive measure in mitigating the transmission of the covid-19 pandemic and also the process of identifying the face if the person is wearing the mask. The increasing need to wear mask in public spaces has resulted in a rising demand for automated solutions to detect face masks. This research presents a novel face mask identification system leveraging Convolutional Neural Networks (CNNs) to accurately detect individuals wearing masks. The proposed CNN-based approach incorporates a deep learning architecture, facilitating the extraction of distinctive facial features for face mask recognition. Training the proposed algorithm involves a substantial dataset comprising diverse images depicting individuals both with and without masks. The deployment of the developed face mask detection technology holds a significant promise for enhancing security, healthcare, and public safety. Its application extend to enforcing mask-wearing regulations, ensuring the well-being of individuals during pandemic situations, and finding utility in real-world scenarios such as airports, hospitals, schools, and public transportation.