Facial Recognition Educational Attendance System

Zhihe Zhang; Zhiqiao He; Kazi Mostafa; Benazir Quadir

doi:10.1007/978-981-96-3949-6_19

Facial Recognition Educational Attendance System

Zhihe Zhang, Zhiqiao He, Kazi Mostafa^*, Benazir Quadir

^*Corresponding author for this work

Xi'an Jiaotong-Liverpool University

Xi'an Jiaotong-Liverpool University

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

Abstract

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.

Original language	English
Title of host publication	Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics
Editors	Wei Chen, Andrew Huey Ping Tan, Yang Luo, Long Huang, Yuyi Zhu, Anwar PP Abdul Majeed, Fan Zhang, Yuyao Yan, Chenguang Liu
Publisher	Springer Singapore
Pages	253-269
Number of pages	17
Volume	1316
ISBN (Electronic)	978-981-96-3949-6
ISBN (Print)	978-981-96-3948-9
DOIs	https://doi.org/10.1007/978-981-96-3949-6_19
Publication status	Published - 2025
Event	2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Suzhou, China Duration: 22 Aug 2024 → 23 Aug 2024

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1316 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024
Country/Territory	China
City	Suzhou
Period	22/08/24 → 23/08/24

Keywords

Facial recognition
attendance systems
deep learning
educational technology
machine learning

Access to Document

10.1007/978-981-96-3949-6_19

Cite this

Zhang, Z., He, Z., Mostafa, K., & Quadir, B. (2025). Facial Recognition Educational Attendance System. In W. Chen, A. H. Ping Tan, Y. Luo, L. Huang, Y. Zhu, A. PP Abdul Majeed, F. Zhang, Y. Yan, & C. Liu (Eds.), Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics (Vol. 1316, pp. 253-269). (Lecture Notes in Networks and Systems; Vol. 1316 LNNS). Springer Singapore. https://doi.org/10.1007/978-981-96-3949-6_19

Zhang, Zhihe ; He, Zhiqiao ; Mostafa, Kazi et al. / Facial Recognition Educational Attendance System. Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics. editor / Wei Chen ; Andrew Huey Ping Tan ; Yang Luo ; Long Huang ; Yuyi Zhu ; Anwar PP Abdul Majeed ; Fan Zhang ; Yuyao Yan ; Chenguang Liu. Vol. 1316 Springer Singapore, 2025. pp. 253-269 (Lecture Notes in Networks and Systems).

@inproceedings{31f23448b4af43428e37e0d34dc968aa,

title = "Facial Recognition Educational Attendance System",

abstract = "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.",

keywords = "Facial recognition, attendance systems, deep learning, educational technology, machine learning",

author = "Zhihe Zhang and Zhiqiao He and Kazi Mostafa and Benazir Quadir",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 ; Conference date: 22-08-2024 Through 23-08-2024",

year = "2025",

doi = "10.1007/978-981-96-3949-6\_19",

language = "English",

isbn = "978-981-96-3948-9",

volume = "1316",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Singapore",

pages = "253--269",

editor = "Wei Chen and \{Ping Tan\}, \{Andrew Huey\} and Yang Luo and Long Huang and Yuyi Zhu and \{PP Abdul Majeed\}, Anwar and Fan Zhang and Yuyao Yan and Chenguang Liu",

booktitle = "Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics",

}

Zhang, Z, He, Z, Mostafa, K & Quadir, B 2025, Facial Recognition Educational Attendance System. in W Chen, AH Ping Tan, Y Luo, L Huang, Y Zhu, A PP Abdul Majeed, F Zhang, Y Yan & C Liu (eds), Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics. vol. 1316, Lecture Notes in Networks and Systems, vol. 1316 LNNS, Springer Singapore, pp. 253-269, 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024, Suzhou, China, 22/08/24. https://doi.org/10.1007/978-981-96-3949-6_19

Facial Recognition Educational Attendance System. / Zhang, Zhihe; He, Zhiqiao; Mostafa, Kazi et al.
Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics. ed. / Wei Chen; Andrew Huey Ping Tan; Yang Luo; Long Huang; Yuyi Zhu; Anwar PP Abdul Majeed; Fan Zhang; Yuyao Yan; Chenguang Liu. Vol. 1316 Springer Singapore, 2025. p. 253-269 (Lecture Notes in Networks and Systems; Vol. 1316 LNNS).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

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 - Facial recognition

KW - attendance systems

KW - deep learning

KW - educational technology

KW - machine learning

UR - https://www.scopus.com/pages/publications/105002731649

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 - 978-981-96-3948-9

VL - 1316

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 Singapore

T2 - 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

Y2 - 22 August 2024 through 23 August 2024

ER -

Zhang Z, He Z, Mostafa K , Quadir B. Facial Recognition Educational Attendance System. In Chen W, Ping Tan AH, Luo Y, Huang L, Zhu Y, PP Abdul Majeed A, Zhang F, Yan Y, Liu C, editors, Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics. Vol. 1316. Springer Singapore. 2025. p. 253-269. (Lecture Notes in Networks and Systems). Epub 2025 Apr 1. doi: 10.1007/978-981-96-3949-6_19

Facial Recognition Educational Attendance System

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