Dela Peņa, Romar N. <br/><br/>
Attendify:  development of facial recognition attendance monitoring system for faculty members using multi-task cascade convolutional neural networks (mtcnn) in computer studies department at tup-manila/ 
Romar N. Dela Peņa, John Michael C. Llosa, and Edzel B. Olaer.-- 
 - Manila:	  Technological University of the Philippines,	  2025. 
 - x, 125pages:  29cm. 
<br/><br/> Bachelor's thesis<br/> 
<br/><br/>College Of Science.--<br/> 
<br/><br/>Includes bibliographic references and index. 
<br/><br/> This study introduces Attendify, a system that uses facial recognition to help track<br/>faculty attendance in the Computer Studies Department of the Technological University of<br/>the Philippines-Manila. Using the MTCNN algorithm, the system provides a quick,<br/>accurate and touchless way to replace fingerprint-based identification. The system was<br/>developed using PHP, MySQL, HTML and JavaScript and it utilized the formal steps of<br/>planning, developing, testing and evaluation. The results showed that Attendify was rated<br/>"Highly Acceptable" in ISO 25010 software quality metrics which means it is appropriate,<br/>usable, reliable, secure and efficient. The system solved issues with manual errors and<br/>paperwork, allowing Attendify to grow and be trusted for future improvements in education<br/>attendance systems. 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Facial recognition<br/>MTCNN<br/>Attendance system
<br/><br/><label>LC Class. No.: </label>BTH QA 76   / D45 2025