International Research Journal of Education and Technology

This project aims to develop an automated system for detecting face masks using Convolutional Neural Networks (CNN), with the goal of enhancing public health safety measures. The main focus is to create a reliable system capable of accurately identifying individuals wearing masks in real-time. The methodology involves training the CNN on a diverse dataset that includes images of people both with and without masks, which is crucial for improving the model's accuracy and robustness. The system is designed to include messaging or email functionalities that alert administrators or relevant authorities when individuals are detected without masks.

This feature facilitates prompt action and compliance monitoring in high-traffic areas such as airports, shopping centers, and public transportation. Initial tests have shown that the CNN model performs well in differentiating between masked and unmasked individuals, indicating its potential as a dependable tool for public health surveillance. Ultimately, this project not only contributes to ongoing public health management efforts during the pandemic but also lays the groundwork for future applications of deep learning in compliance monitoring and safety enforcement. The real-time alerting capability enhances the system's effectiveness, making it a valuable resource for health authorities.The proposed face mask detection and alert system harnesses advanced computer vision and machine learning techniques to identify individuals wearing masks, thereby improving public health safety. By integrating sophisticated machine learning methods with a real-time alert framework, this project provides a thorough solution for organizations aiming to enforce mask-wearing policies efficiently. The system supports the maintenance of public health standards and encourages a proactive safety approach in various settings, including healthcare facilities, public transportation, and crowded venues.

Overall, this face mask detection and alert system signifies a substantial advancement in leveraging technology for public health monitoring and compliance enforcement. It offers precise face mask detection, real-time notifications, and customizable alert settings, making it a dependable solution for monitoring and enforcing mask-wearing policies across

different environments. The architecture of the system features a robust CNN model trained on a varied dataset to ensure high accuracy under diverse conditions. Additionally, the alert system can be tailored, allowing administrators to establish specific thresholds for notifications according to their operational requirements. The project also includes a user-friendly interface for administrators to track compliance statistics and assess system performance.The primary goal of the Face Mask Detection System project is to create an automated solution capable of accurately identifying whether individuals are wearing face masks in real-time. This system will process video streams or images to detect face masks, which is essential for use in public areas such as airports, shopping malls, and public transportation.

Key Words: Face Mask Detection,Convolutional Neural Networks (CNN),Deep Learning.