Leveraging Convolutional Neural Networks for Face Mask Detection

Abstract

In response to the widespread use of face masks for safety, security, and health reasons, the development of automated systems for face mask detection has garnered significant attention. This research addresses binary face mask detection by leveraging Convolutional Neural Networks (CNNs) to create a specialized model. Using a Kaggle dataset, preprocessing steps including image resizing and color space conversion are applied for standardized data. The method entails a purpose-built CNN architecture comprising multiple convolutional layers with ReLU (Rectified Linear Unit) activations and max pooling for efficient feature extraction and spatial information capture. Further bolstering the architecture, fully connected layers coupled with dropout layers mitigate overfitting risks, enhancing generalization. The final sigmoid-activated output layer facilitates precise binary classification, distinguishing individuals with or without masks. Training guided by the Adam optimizer ensures parameter optimization based on accuracy metrics. This work contributes a meticulously designed CNN architecture optimized for face mask detection, showcasing robust feature extraction, spatial complexity handling, and overfitting mitigation, thereby presenting a potent solution with broad implications across industries for safety and public health measures. By aligning technology with societal needs, the research aids diverse industries in integrating automation and Artificial Intelligence for mask-wearing compliance. The findings underscore mask detection's pivotal role in overcoming challenges for safer environments.