FaceGuardVMAPA: Developing an Advanced IoT-Based Facial Recognition System Using Convolutional Neural Networks for Security and Monitoring at Victorino Mapa High School - Angel Danielle F. Cruz; Richelle O. Mendoza; Kurt Lorenz B. Verzosa (APPLIED SCIENCE AND TECHNOLOGY REASERCH JOURNAL, 2025)
The study, titled “FACEGUARDVMAPA: Developing an Advanced IoT-Based Facial Recognition System Using Convolutional Neural Networks for Security and Monitoring at Victorino Mapa High School,” aims to improve security measures and automate student attendance tracking at Victorino Mapa High School. The system leverages Convolutional Neural Networks (CNNs) for facial recognition to facilitate automatic identification and attendance management. To assess its performance, a Likert scale survey based on the ISO 25010 quality model was conducted, focusing on functional suitability, performance efficiency, usability, and security. Feedback from students, parents, and teachers reflected positive reactions, with average satisfaction ratings of 4.41, 4.43, and 4.35, respectively. These results indicate high satisfaction with the system’s features and functionality. Additionally, the inclusion of an SMS notification system, which sends real-time attendance updates to parents, strengthens communication between the school and families. The findings highlight that integrating facial recognition technology and optimized classroom scheduling improves entrance security, enhances attendance monitoring, and supports more efficient resource management. For future improvements, the study suggests the development of more user-friendly interfaces, increased accuracy of the facial recognition algorithm, and the implementation of multi-factor authentication to further enhance security.