000			02037nam a22003017a 4500
003			OSt
005			20250715170237.0
008			250715b |||||||| |||| 00| 0 eng d
040			_aTUPM _bEnglish _cTUPM _dTUPM _erda
050			_aBTH QA 76 _bD45 2025
100			_aDela Peña, Romar N. _eauthor
245			_aAttendify: _bdevelopment of facial recognition attendance monitoring system for faculty members using multi-task cascade convolutional neural networks (mtcnn) in computer studies department at tup-manila/ _cRomar N. Dela Peña, John Michael C. Llosa, and Edzel B. Olaer.--
260			_aManila: _bTechnological University of the Philippines, _c2025.
300			_ax, 125pages: _c29cm.
336			_2rdacontent
337			_2rdamedia
338			_2rdacarrier
500			_aBachelor's thesis
502			_aCollege Of Science.-- _bBachelor of science in computer science: _cTechnological University of the Philippines, _d2025.
504			_aIncludes bibliographic references and index.
520			_aThis study introduces Attendify, a system that uses facial recognition to help track faculty attendance in the Computer Studies Department of the Technological University of the Philippines-Manila. Using the MTCNN algorithm, the system provides a quick, accurate and touchless way to replace fingerprint-based identification. The system was developed using PHP, MySQL, HTML and JavaScript and it utilized the formal steps of planning, developing, testing and evaluation. The results showed that Attendify was rated "Highly Acceptable" in ISO 25010 software quality metrics which means it is appropriate, usable, reliable, secure and efficient. The system solved issues with manual errors and paperwork, allowing Attendify to grow and be trusted for future improvements in education attendance systems.
650			_aFacial recognition
650			_aMTCNN
650			_aAttendance system
700			_aLlosa, John Michael C. _eauthor
700			_aOlaer, Edzel B. _eauthor
942			_2lcc _cBTH COS _n0
999			_c30367 _d30367