Real-Time Facial Cover Detection System based Deep Convolution Neural Network with Proposed Activation Function

The ongoing global contagion has highlighted the importance of effective preventive measures such as wearing face masks in public spaces. In this study, we suggest a deep learning-based approach for real-time facial covering detection to aid in enforcing mask-wearing protocols. Our system utilizes deep learning networks (CNNs) to automatically detect whether individuals in images or video streams are wearing mask or not. The suggested system includes of 3 main stages: face detection, facial cover detection, 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 appraise the execution 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 as system in real-world settings, including issues related to privacy, bias, and scalability. Overall, our proposed facial covering detection system offers a viable solution for automated monitoring and enforcement of face mask policies, contributing to public health efforts in mitigating the spread of contagious diseases.