An Intelligent System for Face Mask Recognition and Non-Contact Temperature Detection Using Deep Learning

The increasing prevalence of airborne infectious diseases has necessitated the development of intelligent, automated screening systems to ensure public safety in high-risk environments. This paper proposes a deep learning and neural network-based approach for integrated face mask detection and non-contact temperature identification. The system is designed to operate in real-time and is suitable for deployment in public spaces such as transportation terminals, corporate entry points, and commercial facilities. To enhance identification accuracy under mask-wearing conditions, the proposed framework employs a masked facial recognition technique that utilizes convolutional neural networks (CNNs) to analyze visible facial features. In parallel, a contactless infrared temperature sensor enables real-time thermal screening without human intervention. The system architecture is supported by a lightweight, reliable IoT communication protocol for efficient data transmission to a cloud-based platform, enabling remote monitoring through web and mobile applications. Experimental validation demonstrates that the proposed system achieves high accuracy in both mask detection and temperature measurement, outperforming conventional manual methods in terms of speed, reliability, and scalability. The collected data can be further utilized for health analytics and decision support by relevant authorities. This work contributes a cost-effective, scalable solution for enhancing public health safety through automation and intelligent sensing.