TY - GEN
T1 - Facial Recognition Educational Attendance System
AU - Zhang, Zhihe
AU - He, Zhiqiao
AU - Mostafa, Kazi
AU - Quadir, Benazir
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - Using quick facial recognition to enable smart attendance is a practical way to manage everyday tasks and keep track of student attendance. Face biometrics, based on high-resolution monitor footage and other technologies, is used by face recognition-based attendance systems to identify students by their faces. In this study, we designed and implemented a machine learning-based facial recognition academic attendance system, aiming to improve the efficiency and accuracy of attendance management in educational institutions. The system automatically recognizes student attendance by utilizing efficient facial recognition technology, thereby reducing the labour cost and time consumption of traditional attendance methods. Firstly, we collected and Firstly, we collected and preprocessed a large amount of facial data, and trained a high-precision facial recognition model using deep learning algorithms. Subsequently, the model was integrated into a user-friendly attendance management system. Model was integrated into a user-friendly attendance system, supporting real-time attendance recording and data management. The system test results show that our facial recognition model has achieved excellent performance in accuracy and recognition speed.
AB - Using quick facial recognition to enable smart attendance is a practical way to manage everyday tasks and keep track of student attendance. Face biometrics, based on high-resolution monitor footage and other technologies, is used by face recognition-based attendance systems to identify students by their faces. In this study, we designed and implemented a machine learning-based facial recognition academic attendance system, aiming to improve the efficiency and accuracy of attendance management in educational institutions. The system automatically recognizes student attendance by utilizing efficient facial recognition technology, thereby reducing the labour cost and time consumption of traditional attendance methods. Firstly, we collected and Firstly, we collected and preprocessed a large amount of facial data, and trained a high-precision facial recognition model using deep learning algorithms. Subsequently, the model was integrated into a user-friendly attendance management system. Model was integrated into a user-friendly attendance system, supporting real-time attendance recording and data management. The system test results show that our facial recognition model has achieved excellent performance in accuracy and recognition speed.
KW - attendance systems
KW - deep learning
KW - educational technology
KW - Facial recognition
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=105002731649&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-3949-6_19
DO - 10.1007/978-981-96-3949-6_19
M3 - Conference Proceeding
AN - SCOPUS:105002731649
SN - 9789819639489
T3 - Lecture Notes in Networks and Systems
SP - 253
EP - 269
BT - Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics
A2 - Chen, Wei
A2 - Ping Tan, Andrew Huey
A2 - Luo, Yang
A2 - Huang, Long
A2 - Zhu, Yuyi
A2 - PP Abdul Majeed, Anwar
A2 - Zhang, Fan
A2 - Yan, Yuyao
A2 - Liu, Chenguang
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024
Y2 - 22 August 2024 through 23 August 2024
ER -