| 000 -LEADER |
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| fixed length control field |
02630nam a22002417a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
ft8837 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251128105516.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
251128b ||||| |||| 00| 0 eng d |
| 041 ## - LANGUAGE CODE |
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| Language code of text/sound track or separate title |
engtag |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
T58.5 C78 2025 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
. |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Cruz, Angel Danielle F.; Mendoza, Richelle O.; Verzosa, Kurt Lorenz B. |
| 245 ## - TITLE STATEMENT |
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| Title |
Faceguardvmapa: Developing an advanced Iot-based facial recognition system using convolutional neural networks for security and monitoring at Victorino Mapa High School |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Place of production, publication, distribution, manufacture |
. |
| Name of producer, publisher, distributor, manufacturer |
. |
| Date of production, publication, distribution, manufacture, or copyright notice |
c2025 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Other physical details |
Capstone Project: (Bachelor of Science in Information Technology) - Pamantasan ng Lungsod ng Maynila, 2025 |
| 336 ## - CONTENT TYPE |
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| Source |
text |
| Content type term |
text |
| Content type code |
text |
| 337 ## - MEDIA TYPE |
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| Source |
unmediated |
| Media type term |
unmediated |
| Media type code |
unmediated |
| 338 ## - CARRIER TYPE |
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| Source |
volume |
| Carrier type term |
volume |
| Carrier type code |
volume |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
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. |
| 526 ## - STUDY PROGRAM INFORMATION NOTE |
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| Classification |
Filipiniana |
| 655 ## - INDEX TERM--GENRE/FORM |
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| Genre/form data or focus term |
academic writing |
| 942 ## - ADDED ENTRY ELEMENTS |
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| Source of classification or shelving scheme |
|
| Item type |
Thesis/Dissertation |