Open AccessIntelligent Attendance System with Face Recognition using the Deep Convolutional Neural Network Method
Author(s) -
Nurkhamid,
Pradana Setialana,
Handaru Jati,
Ratna Wardani,
Yuniar Indrihapsari,
Norita Md Norwawi
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1737/1/012031
Subject(s) - attendance , convolutional neural network , computer science , facial recognition system , multimedia , face (sociological concept) , deep learning , artificial intelligence , artificial neural network , the internet , machine learning , world wide web , pattern recognition (psychology) , social science , sociology , economics , economic growth
Recording student attendance in lectures can be done in several ways, namely giving initials on the attendance sheet or by the lecturer calling each student and then giving a checkmark on the attendance sheet or attendance recording system. This method is inefficient because it is done repeatedly at every meeting, resulting in reduced lecturing time. Some researchers are trying to develop various ways to overcome this, such as using fingerprints, Internet of Things devices, cards with RFID technology, QR codes, and smartphones. However, these technologies require many devices, and they may be costly. The purpose of this research is to develop an intelligent attendance system with facial recognition technology that can identify many people simultaneously without having to make direct contact using the Deep Convolutional Neural Network method. The system is then tested and analyzed for its accuracy in identifying and recording student attendance. The results of research conducted on 16 students in a lecture show that the system can be used to record student attendance with an accuracy of 81.25% in the condition that the student facing forward, 75.00% in the student condition facing sideways, and 43.75% in the student condition facing down.