Enhancing Public Health Measures: A Deep Learning Approach for Real-Time Facemask Recognition to Mitigate the Spread of Infectious Diseases

This paper presents a system for real-time facemask recognition, integrating automation through deep learning to enhance public health measures and mitigate the spread of infectious diseases. The proposed system employs OpenCV’s Haar Cascade Classifier for face detection, establishing the foundational framework for subsequent processes. The heart of the system lies in its Deep Learning Model, consisting of two critical components: Data PreProcessing and CNN Training. The former involves meticulous steps such as resizing, color space conversion, and normalization, while incorporating data augmentation techniques to ensure a diverse and robust training dataset. The latter employs convolutional neural networks (CNNs) to autonomously learn and discern facemask presence, contributing to the system’s accuracy. The User Interface (UI) component, built using tkinter, provides a visual representation of the video stream, displaying real-time face mask detection results. The system’s scalability and efficiency are heightened by its automated nature, eliminating the need for human intervention. Furthermore, the UI serves as a user-friendly interface, potentially expanding to include features like data logging and reporting. The Notification System, integrated using the ‘plyer’ library, enhances user engagement by providing real-time alerts on the desktop. These notifications act as reminders for individuals not adhering to mask guidelines, fostering immediate compliance. The proposed system boasts several advantages, including automation, scalability, adaptability to diverse conditions, security against spoofing attacks, cost reduction, improved accuracy, and increased flexibility. Methodologically, the research adopts an applied research methodology, combining empirical, experimental, and practical research elements to develop and deploy an effective solution. At the end of the work, a real-time face mask recognition system, that is capable of analysing live video feeds from public spaces; detect mask-wearing violations, and issue alerts when necessary, was achieved. Notably, the deep learning model consistently demonstrated high accuracy, effectively minimizing both false positives and false negatives.