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

Son, Ngo Tung; Anh, Bùi Ngọc; Phuong, Le; Chien, Bui Dinh; Hoa, Duong Xuan; Thanh, Le Van; Huy, Trần Quang; Duy, Lê Đình; Khan, Muhammad Hassan Raza

doi:10.3390/sym12020307

Cited by 19 publications

(14 citation statements)

References 24 publications

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“…Some authors propose solutions that can be integrated with schools CCTV systems to record attendance of lectures or school in general (Son et al, 2020). Figure 2 illustrates proposed components of face recognition module.…”

Section: Methodsmentioning

confidence: 99%

Deep learning solution for children long-term identification

Bumanis¹,

Vītols²,

Arhipova³

et al. 2020

Research for Rural Development

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Deep learning algorithms are becoming default solution for application in business processes where recognition, identification and automated learning are involved. For human identification, analysis of various features can be applied. Face feature analysis is most popular method for identification of person in various stages of life, including children and infants. The aim of this research was to propose deep learning solution for long-term identification of children in educational institutions. Previously proposed conceptual model for long-term re-identification was enhanced. The enhancements include processing of unexpected persons’ scenarios, knowledge base improvements based on results of supervised and unsupervised learning, implementation of video surveillance zones within educational institutions and object tracking results’ data chaining between multiple logical processes. Object tracking results are the solution we found for long-term identification realization.

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Section: Methodsmentioning

confidence: 99%

Deep learning solution for children long-term identification

Bumanis¹,

Vītols²,

Arhipova³

et al. 2020

Research for Rural Development

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“…It is important to notice that the second most largest category goes to private datasets with 34 papers (Zhou et al, 2018;Ara et al, 2017;Phankokkruad, 2018;Gilani and Mian, 2018;Khan et al, 2019b;Qin et al, 2019;Liu et al, 2019;Peng et al, 2019;Mangal et al, 2020;Lv et al, 2020;Perti et al, 2020;Kim et al, 2017;Irjanto and Surantha, 2020;Arafah et al, 2020;Prasetyo et al, 2021;Moon et al, 2017;Chandran et al, 2018;Yang et al, 2018;Son et al, 2020;Alhanaee et al, 2021;Khan et al, 2020;Nakajima et al, 2021;Talahua et al, 2021;He and Ding, 2023;Karlupia et al, 2023;Bussey et al, 2017;Li et al, 2022;Filippidou and Papakostas, 2020;Bussey et al, 2017;Singh et al, 2022;Setio Aji et al, 2022;Wang et al, 2022;Lestari et al, 2021) creating their own datasets for testing. There are advantages and disadvantages to developing and utilizing private face image datasets for CNN-based face recognition.…”

Section: Assessment Of Q3: What Type Of Cnn Model Is Most Commonly Us...mentioning

confidence: 99%

Convolutional Neural Networks for Face Recognition: A Systematic Literature Review

Barcic¹,

Grd²,

Tomičić³

2023

Preprint

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This paper presents a comprehensive overview of Convolutional Neural Networks (CNNs) in the context of face recognition. By analyzing 150 research papers, we investigate major publication channels, temporal trends, prevalent CNN models, primary data sets, accuracy levels, primary research focuses, and future prospects for improving CNN-based facial recognition. A major focus is placed on identifying prevalent CNN architectures, techniques used for facial recognition and shedding light on the evolving landscape of CNN designs. Furthermore, we examine the datasets used for training and testing CNNs, and evaluate the accuracy levels achieved by these models. Lastly, we discuss future directions for enhancing CNN-based facial recognition, including addressing bias and fairness, improving robustness to environmental variations, privacy preservation, and exploring transfer learning and multimodal fusion. This paper serves as a valuable resource, summarizing major trends in CNN-based face recognition. It provides insights for researchers and practitioners, guiding future advancements in this rapidly evolving field.

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“…The researcher states that the total accuracy of the proposed system is 99.5%. [46]. The models used are MTCNN, FaceNet, and ArcFace.…”

Section: Khan Et Al Used a Convolutionalmentioning

confidence: 99%

A Survey of Face Recognition based on Convolutional Neural Network

Saragih

2022

Indonesian J. of Inf. Syst.

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Face recognition is one of the interesting research topics in the field of computer vision. In recent years, deep learning methods, especially the Convolutional Neural Network, have progressed. One of the successes of CNN is in face recognition. Face recognition by computer is a technique done so that the computer can automatically recognize faces in an image. Various researchers have conducted related research on facial recognition. This survey presents researches related to face recognition based on Convolutional Neural Network that has been conducted. The studies used are studies that have been published in the last five years. It was performed to determine the renewal that emerged in face recognition based on Convolutional Neural Network. The basic theory of the Convolutional Neural Network, face recognition, and description of the database used in various researches are also discussed. Hopefully, this survey can provide additional knowledge regarding face recognition based on the Convolutional Neural Network.

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

Cited by 19 publications

References 24 publications

Deep learning solution for children long-term identification

Deep learning solution for children long-term identification

Convolutional Neural Networks for Face Recognition: A Systematic Literature Review

A Survey of Face Recognition based on Convolutional Neural Network

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