Proposed Activation Function Based Deep Learning Approach for Real- Time Face Mask Detection System

The ongoing global pandemic has underscored the importance of effective preventive measures such as wearing face masks in public spaces. In this paper, we propose a deep learning-based approach for real-time face mask detection to aid in enforcing mask-wearing protocols. Our system utilizes convolutional neural networks (CNNs) to automatically detect whether individuals in images or video streams are wearing masks or not. The proposed system consists of three main stages: face detection, face mask classification, and real-time monitoring. Firstly, faces are localized in the input image or video frame using a proposed face detection model. Then, the detected faces are fed into a proposed CNN model for mask classification, which determines whether each face is covered with a mask or not. Finally, the system will provide real-time monitoring and alerts authorities or stakeholders about non-compliance with mask-wearing guidelines. We evaluate the performance of our system on publicly available datasets and demonstrate its effectiveness in accurately detecting face masks in various scenarios. Additionally, we discuss the challenges and limitations of deploying such a system in real-world settings, including issues related to privacy, bias, and scalability. Overall, our proposed face mask detection system offers a promising solution for automated monitoring and enforcement of face mask policies, contributing to public health efforts in mitigating the spread of contagious diseases.