000			02630nam a22002417a 4500
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008			251128b ||||| |||| 00| 0 eng d
041			_aengtag
050			_aT58.5 C78 2025
082			_a.
100	1		_aCruz, Angel Danielle F.; Mendoza, Richelle O.; Verzosa, Kurt Lorenz B.
245			_aFaceguardvmapa: Developing an advanced Iot-based facial recognition system using convolutional neural networks for security and monitoring at Victorino Mapa High School
264		1	_a. _b. _cc2025
300			_bCapstone Project: (Bachelor of Science in Information Technology) - Pamantasan ng Lungsod ng Maynila, 2025
336			_2text _atext _btext
337			_2unmediated _aunmediated _bunmediated
338			_2volume _avolume _bvolume
505			_aABSTRACT: 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.
526			_aF
655			_aacademic writing
942			_2lcc _cMS
999			_c37249 _d37249