Face Mask Detection Using Deep Learning

Authors: Rohan Chaudhary, Parul Tyagi, Neetu Maurya

Abstract: The COVID-19 pandemic emphasized the importance of face masks as an effective preventive measure to reduce the spread of airborne diseases. Ensuring proper mask usage in public areas through manual monitoring is inefficient, time-consuming, and prone to human error, especially in crowded environments. This paper proposes an automated face mask detection system based on deep learning and computer vision techniques to address these challenges. The system utilizes transfer learning with the MobileNetV2 architecture, selected for its lightweight design and high accuracy, making it suitable for real-time applications. OpenCV’s Haar Cascade classifier is employed for rapid face detection, after which the detected facial regions are classified into three categories: mask worn correctly, mask worn incorrectly, and no mask. Image preprocessing and data augmentation techniques are applied to enhance model generalization under varying lighting and pose conditions. The model is trained and evaluated using standard performance metrics, achieving high accuracy with minimal latency. The proposed system enables real-time monitoring and can be deployed in public spaces such as hospitals, transportation hubs, educational institutions, and workplaces. This approach reduces reliance on manual supervision and provides a scalable, cost-effective solution for automated health-safety monitoring.

DOI: http://doi.org/10.5281/zenodo.20339146