Development of Contactless Entry System with Facemask Detection using Tensor Flow (Api) Application Program Interface

This study aimed to design a kiosk-based contactless entry system to minimize manual form completion and trace potential COVID-19 carriers at the Mariano Marcos State University, College of Engineering. The system sought to enhance convenience for students entering the college by incorporating features such as facemask detection, RFID scanning, QR code scanning, and temperature scanning. Facemask detection and temperature sensors were used to identify possible COVID-19 carriers, while RFID and QR codes were employed for contact tracing by collecting user data. A portable, slim design was chosen to ensure ease of deployment. Additionally, a web-based system was developed where students and guests could access their accounts, register, and generate QR codes for seamless entry. To evaluate the effectiveness of the system, functionality tests were conducted on all its features. Researchers also gathered feedback from students to better understand any limitations of the system. The evaluation results were exceptional, showing near-perfect scores across all areas of functionality, convenience, and accuracy. This positive outcome demonstrated that the system significantly streamlined the entry process while maintaining strict health protocols, thereby reducing potential virus transmission. Based on the collected data, the study concluded that the developed kiosk-based contactless entry system was highly effective and successful in achieving its goals of enhancing safety and efficiency for students and visitors at the College of Engineering.