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Symmetry
Volume 12
Issue 2
10.3390/sym12020307
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Article

Implementing CCTV-Based Attendance Taking Support System Using Deep Face Recognition: A Case Study at FPT Polytechnic College

by
Ngo Tung Son
1,*,
Bui Ngoc Anh
1,
Tran Quy Ban
1,
Le Phuong Chi
1,
Bui Dinh Chien
1,
Duong Xuan Hoa
1,
Le Van Thanh
1,
Tran Quang Huy
1,
Le Dinh Duy
1 and
Muhammad Hassan Raza Khan
2
1
Information and Communication Technology Department, FPT University, Hanoi 100000, Vietnam
2
Mathematical Department, University of Padova, 35100 Padova, Italy
*
Author to whom correspondence should be addressed.
Symmetry 2020, 12(2), 307; https://doi.org/10.3390/sym12020307
Submission received: 24 January 2020 / Revised: 16 February 2020 / Accepted: 17 February 2020 / Published: 21 February 2020
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Abstract

Face recognition (FR) has received considerable attention in the field of security, especially in the use of closed-circuit television (CCTV) cameras in security monitoring. Although significant advances in the field of computer vision are made, advanced face recognition systems provide satisfactory performance only in controlled conditions. They deteriorate significantly in the face of real-world scenarios such as lighting conditions, motion blur, camera resolution, etc. This article shows how we design, implement, and conduct the empirical comparisons of machine learning open libraries in building attendance taking (AT) support systems using indoor security cameras called ATSS. Our trial system was deployed to record the appearances of 120 students in five classes who study on the third floor of FPT Polytechnic College building. Our design allows for flexible system scaling, and it is not only usable for a school but a generic attendance system with CCTV. The measurement results show that the accuracy is suitable for many different environments.
Keywords: face recognition; CCTV; attendance taking system; deep learning; computer vision face recognition; CCTV; attendance taking system; deep learning; computer vision

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MDPI and ACS Style

Son, N.T.; Anh, B.N.; Ban, T.Q.; Chi, L.P.; Chien, B.D.; Hoa, D.X.; Thanh, L.V.; Huy, T.Q.; Duy, L.D.; Hassan Raza Khan, M. Implementing CCTV-Based Attendance Taking Support System Using Deep Face Recognition: A Case Study at FPT Polytechnic College. Symmetry 2020, 12, 307. https://doi.org/10.3390/sym12020307

AMA Style

Son NT, Anh BN, Ban TQ, Chi LP, Chien BD, Hoa DX, Thanh LV, Huy TQ, Duy LD, Hassan Raza Khan M. Implementing CCTV-Based Attendance Taking Support System Using Deep Face Recognition: A Case Study at FPT Polytechnic College. Symmetry. 2020; 12(2):307. https://doi.org/10.3390/sym12020307

Chicago/Turabian Style

Son, Ngo Tung, Bui Ngoc Anh, Tran Quy Ban, Le Phuong Chi, Bui Dinh Chien, Duong Xuan Hoa, Le Van Thanh, Tran Quang Huy, Le Dinh Duy, and Muhammad Hassan Raza Khan. 2020. "Implementing CCTV-Based Attendance Taking Support System Using Deep Face Recognition: A Case Study at FPT Polytechnic College" Symmetry 12, no. 2: 307. https://doi.org/10.3390/sym12020307

APA Style

Son, N. T., Anh, B. N., Ban, T. Q., Chi, L. P., Chien, B. D., Hoa, D. X., Thanh, L. V., Huy, T. Q., Duy, L. D., & Hassan Raza Khan, M. (2020). Implementing CCTV-Based Attendance Taking Support System Using Deep Face Recognition: A Case Study at FPT Polytechnic College. Symmetry, 12(2), 307. https://doi.org/10.3390/sym12020307

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