Face Mask Detection Using Deep Learning: A Comparative Study of CNN, VGG16 and MobileNetV2 for Real-Time Applications
TL;DR
A comparative study of three deep learning architectures, namely a Custom Convolutional Neural Network (CNN), VGG16, and MobileNetV2, for face mask detection indicates that lightweight transfer learning models offer an effective and practical solution for real-time face mask detection in resource-constrained environments.
Abstract
The COVID-19 pandemic highlighted the importance of face masks as an effective non-pharmaceutical intervention for reducing the transmission of infectious diseases. Monitoring mask compliance in public environments such as hospitals, educational institutions, transportation hubs, and workplaces remains a challenging task when performed manually. Recent advances in computer vision and deep learning have enabled the development of automated face mask detection systems capable of operating in real time. This paper presents a comparative study of three deep learning architectures, namely a Custom Convolutional Neural Network (CNN), VGG16, and MobileNetV2, for face mask detection. The study employs a publicly available dataset containing 12,000 facial images categorized into mask and no-mask classes. Data preprocessing techniques including resizing, normalization, and augmentation were applied to improve model generalization. Experimental results demonstrate that MobileNetV2 outperforms the other architectures, achieving an accuracy of 98.7%, precision of 98.4%, recall of 99.0%, and an AUC-ROC score of 0.99 while maintaining real-time performance. The proposed system was further integrated with OpenCV for live video stream analysis. The findings indicate that lightweight transfer learning models offer an effective and practical solution for real-time face mask detection in resource-constrained environments.