ISBN: 978-981-18-5852-9 DOI: 10.18178/wcse.2022.04.014
Personnel Face Recognition Access and Automation System Design for University Engineering Laboratories
Abstract— Security and crowd control have become a pressing concern in times of the pandemic because of health and safety reasons in populated areas. Imposed restrictions by the authorities to limit people's activities cannot be monitored with a few security personnel such as in schools and universities. Current CCTV systems can monitor and record activities in crowded premises but may not be able to control entry and exit in the school or universities. Face recognition technology is applied in this research for keyless automated laboratory access to monitor personnel activity using security IP cameras and control laboratory occupants. The algorithm of Principal Component Analysis (PCA) for face detection using Emgu CV libraries in a computer provided additional relevant functionalities to the existing security IP cameras when connected to the PC. The studied system has fast non-contact automated door access with minimal user interaction and is programmed to include an attendance system for personnel. It also provided automated ventilation and lighting in the laboratory rooms for efficient energy utilization and provisions for alarm when a crowd is detected. The system provided an added level of security for populated premises such as schools or universities in as much as population safety and crowd monitoring is required. When two or more faces are adjacent and detected, the crowd control is triggered with a message to observe minimum social distancing. The implemented system showed detection reliability of up to 96.67% using Machine learning technology. Results showed significant added functionality to the CCTV security system. Internet of Things and Artificial Intelligence in biometrics improved the reliability of fast and efficient facial recognition authentication, automation and security surveillance systems. The smart door for restricted environments significantly increases the safety of workplaces and schools when implemented with IoT devices such as Arduino and digital IP cameras.
Index Terms— machine learning, AI, biometrics, facial recognition, crowd control.
University of Mindanao
University of Mindanao
Cite: Bernard Camacho, Rolieven Cañizares, "Personnel Face Recognition Access and Automation System Design for University Engineering Laboratories," WCSE 2022 Spring Event: 2022 9th International Conference on Industrial Engineering and Applications, pp. 113-121, Sanya, China, April 15-18, 2022.