Deep Learning Based Attendance Check System At FPT University

Le, CH; Ly, HP; Nguyen, DT; Dang-Ngoc, H; Vu, TDT; Dang, DNM

Deep Learning Based Attendance Check System At FPT University

Le, CH Ly, HP Nguyen, DT Dang-Ngoc, H Vu, TDT Dang, DNM

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Publisher:: ACM
Publication Type:: Conference Proceeding
Citation:: ICIIT '24: Proceedings of the 2024 9th International Conference on Intelligent Information Technology, 2024, pp. 272-281
Issue Date:: 2024-02-23

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Field	Value	Language
dc.contributor.author	Le, CH
dc.contributor.author	Ly, HP
dc.contributor.author	Nguyen, DT
dc.contributor.author	Dang-Ngoc, H
dc.contributor.author	Vu, TDT
dc.contributor.author	Dang, DNM
dc.date	2024-02-23
dc.date.accessioned	2025-03-13T22:33:18Z
dc.date.available	2025-03-13T22:33:18Z
dc.date.issued	2024-02-23
dc.identifier.citation	ICIIT '24: Proceedings of the 2024 9th International Conference on Intelligent Information Technology, 2024, pp. 272-281
dc.identifier.isbn	979-8-4007-1671-3
dc.identifier.uri	http://hdl.handle.net/10453/185791
dc.description.abstract	In the era of Vietnam’s Industrial Revolution 4.0, technological advancements are crucial, especially in computing and telecommunications. Digital images and videos have become indispensable tools in the modern information age. Human faces hold significant value as biometric identifiers in image and video databases used for surveillance systems. The foundation of model-automated attendance systems utilizing facial recognition is based on taking a face image captured by the camera as input and producing a text recording of the person’s presence. Our study focuses on developing an automatic attendance system based on face recognition using deep learning techniques for real-time recognition. The system includes image capture from a webcam, face detection, dataset management, face recognition, and attendance recording. We investigated and compared three different face recognition models: the VGG-Face model, the Siamese network model, and the Inception-ResNet-v2 model pre-trained. The Siamese Network model achieved the highest accuracy of 94.1% on the LFW dataset. On the other hand, we also trained this enhanced Siamese Network model on the VNCeleb dataset specifically for testing on our system to be used for FPT University students - Vietnamese students. This enhanced Siamese Network model is integrated into the system, achieving 94.4% accuracy on the FPT University student dataset. This system streamlines attendance recording by eliminating the need for manual attendance taking, thereby minimizing human error and saving time.
dc.language	en
dc.publisher	ACM
dc.relation.ispartof	ICIIT '24: Proceedings of the 2024 9th International Conference on Intelligent Information Technology
dc.relation.ispartof	International Conference on Intelligent Information Technology
dc.relation.ispartofseries	ACM International Conference Proceeding Series
dc.relation.isbasedon	10.1145/3654522.3654584
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Deep Learning Based Attendance Check System At FPT University
dc.type	Conference Proceeding
utslib.location.activity	Ho Chi Minh City Vietnam
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2025-03-13T22:33:11Z
pubs.finish-date	2024-02-25
pubs.place-of-publication	USA
pubs.publication-status	Published
pubs.start-date	2024-02-23
dc.location	USA

Abstract:

In the era of Vietnam’s Industrial Revolution 4.0, technological advancements are crucial, especially in computing and telecommunications. Digital images and videos have become indispensable tools in the modern information age. Human faces hold significant value as biometric identifiers in image and video databases used for surveillance systems. The foundation of model-automated attendance systems utilizing facial recognition is based on taking a face image captured by the camera as input and producing a text recording of the person’s presence. Our study focuses on developing an automatic attendance system based on face recognition using deep learning techniques for real-time recognition. The system includes image capture from a webcam, face detection, dataset management, face recognition, and attendance recording. We investigated and compared three different face recognition models: the VGG-Face model, the Siamese network model, and the Inception-ResNet-v2 model pre-trained. The Siamese Network model achieved the highest accuracy of 94.1% on the LFW dataset. On the other hand, we also trained this enhanced Siamese Network model on the VNCeleb dataset specifically for testing on our system to be used for FPT University students - Vietnamese students. This enhanced Siamese Network model is integrated into the system, achieving 94.4% accuracy on the FPT University student dataset. This system streamlines attendance recording by eliminating the need for manual attendance taking, thereby minimizing human error and saving time.

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http://hdl.handle.net/10453/185791