Faceguardvmapa: Developing an advanced Iot-based facial recognition system using convolutional neural networks for security and monitoring at Victorino Mapa High School

ABSTRACT: 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,” addresses the need for enhanced security and automated attendance tracking within the school premises. Traditional attendance systems, which rely on manual input, are prone to inaccuracies, inefficiencies, and unauthorized access. To mitigate these issues, the system leverages Convolutional Neural Networks (CNNs) for real-time facial recognition, ensuring accurate and efficient student identification while reducing administrative workload. A sophisticated IoT-powered facial recognition framework aimed at improving campus safety, streamlining attendance tracking, and optimizing the use of school resources. It tackles pressing issues such as lax entry security, unreliable manual attendance systems, poor parent-school communication regarding student movements, and scheduling conflicts in classroom usage. By implementing CNN-driven facial recognition at entry points, the system strengthens access control and minimizes unauthorized access. Additionally, it features an SMS notification system that instantly alerts parents when their children arrive at or leave the school, enhancing safety and communication. The solution also integrates an automated attendance tracker within classrooms, reducing human error and improving data accuracy. Finally, predictive analytics is used to optimize classroom scheduling and resource allocation. Overall, the implementation of FACEGUARDVMAPA demonstrates significant gains in campus security, operational efficiency, and faculty communication, providing a replicable model for similar educational institutions.

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