Real Time Face Mask Detection using Deep Learning Approach

Abstract

In response to the need for face masks in preventing dangerous illnesses like COVID-19, this paper is centered around deep learning-based face mask identification. Leveraging powerful libraries such as TensorFlow, Keras, and OpenCV, our objective is to create a reliable and effective system capable of discerning individuals wearing face masks from those who are not. This innovative approach holds immense potential for applications in public health monitoring, security systems, and critical public spaces where adherence to face mask policies is of utmost importance.To ensure practical usability, we deploy the trained model in real-time scenarios, utilizing webcams or video streams. The system efficiently processes frames, swiftly detecting and classifying faces with or without masks, and promptly providing feedback or alerts to users or surveillance systems.This paper highlights how well TensorFlow, Keras, and OpenCV work together to create a reliable and accurate face mask detection system. The amalgamation of deep learning, image processing, and real-time capabilities facilitates seamless monitoring of face mask adherence, significantly contributing to public safety and health initiatives across various domains.