Paper Contents
Abstract
The Face Mask Detection System is an AI-driven solution designed to ensure mask compliance in public and private spaces. Using a combination of computer vision and deep learning, the system detects human faces in real-time and classifies them as "masked" or "unmasked." The project employs OpenCV for face detection, a Convolutional Neural Network (CNN) for mask classification, and Flask for the web interface. When an unmasked individual is detected, an alert system, integrated with SMTP, sends email notifications. The system achieves 98.5% accuracy in mask classification and 95% accuracy in face detection, operating at 15-20 FPS to provide real-time feedback. Key features include a real-time video feed, a configurable alert mechanism, and a user-friendly web interface. This system has potential applications in healthcare facilities, workplaces, public transport, and other high-traffic areas to enhance health and safety measures. Future enhancements may include multi-camera support, cloud-based deployment, and improved model performance through transfer learning.Keywords: Face Mask Detection, Convolutional Neural Network (CNN), Deep Learning, Computer Vision, OpenCV, Real-Time Detection, COVID-19 Safety, Image Classification, Artificial Intelligence (AI), Object Detection.
Copyright
Copyright © 2025 Nathiya A. This is an open access article distributed under the Creative Commons Attribution License.
