Face Recognition from Surveillance Using Sequnetial CNN-Model

Perti, Ashwin; Singh, Mukul; Panwar, Harsh; Tyagi, Harsh

doi:10.1109/icrito48877.2020.9197916

Cited by 2 publications

(5 citation statements)

References 6 publications

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“…In addition, there were 3 smaller categories of Siamese CNN (Perti et al, 2020;Steven et al, 2020), Lightweight CNN (Yu et al, 2018) and Low-power CNN (Bong et al, 2018).…”

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

confidence: 99%

“…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%

“…Some of the analyzed papers have put their focus on face recognition for surveillance, like (Herrmann et al, 2016) where the authors presented a face recognition method based on a CNN with a manifold-based track comparison strategy for low-resolution video face recognition. (Perti et al, 2020) used a DCNN and Siamese network to train images to classify into customers, important customers, and employees from cameras. They create three models, the first one using MTCNN, a DCNN, and a Siamese network, the second model uses a three-layer CNN model, and the third model relies on a pre-trained CGG-16 model.…”

Section: 60%mentioning

confidence: 99%

“…The results are shown in Table 11. al, 2022;Taherkhani et al, 2019;Guo et al, 2017;Qin et al, 2019;Filippidou and Papakostas, 2020;Perti et al, 2020;Sepas-Moghaddam et al, 2020;Zhiqi, 2021;Tran et al, 2022;Luttrell et al, 2017;Nam et al, 2018;Chandran et al, 2018;Choi and Lee, 2020;Zangeneh et al, 2020;Kim et al, 2020) ResNet (He et al, 2016) (Ling et al, 2020;Li et al, 2022;Horng et al, 2022;Li et al, 2022;Kim et al, 2020;Feng et al, 2020;Arafah et al, 2020;Almabdy and Elrefaei, 2019;Setio Aji et al, 2022;Gruber et al, 2017;Hou et al, 2020;Filippidou and Papakostas, 2020;Alkanhal et al, 2023;Zhou et al, 2020) AlexNet (Krizhevsky et al, 2012) (Bukovčiková et al, 2017;Bussey et al, 2017;Liu et al, 2017;Khan et al, 2019b;Han, 2021;H et al, 2023;Almabdy and Elrefaei, 2019;Ma et al, 2018;Alhan...…”

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

confidence: 99%

“…A Siamese network is a special type of CNN architecture commonly used for tasks involving similarity or distance comparisons between pairs of inputs (Perti et al, 2020;Steven et al, 2020). It is made up of two or more subnetworks that are identical and have the same architecture and weights.…”

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

confidence: 99%

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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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“…In addition, there were 3 smaller categories of Siamese CNN (Perti et al, 2020;Steven et al, 2020), Lightweight CNN (Yu et al, 2018) and Low-power CNN (Bong et al, 2018).…”

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

confidence: 99%

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

confidence: 99%

Section: 60%mentioning

confidence: 99%

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

confidence: 99%

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

confidence: 99%

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Convolutional Neural Networks for Face Recognition: A Systematic Literature Review

Barcic¹,

Grd²,

Tomičić³

2023

Preprint

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Biometric Identification Using Face Mask DL and Open CV

Rastogi,

Varshney,

Jaiswal

et al. 2023

Pioneering Smart Healthcare 5.0 With IoT, Federated Learning, and Cloud Security

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COVID-19 is a disease which spreads with human-to-human interaction. In this situation, humans need to be distanced from each other. To prevent the pandemic, people need to wear masks. Wearing a mask is mandatory for all people. That's why it is important to detect whether a person is wearing it or not. This chapter aims to provide a biometric approach for COVID 19 prevention. In this research the machine is able to check whether a person is wearing a mask or not from an image or a live stream. This research is also a smart approach for smart cities. In this research, the authors generate the artificial dataset by adding the mask on the faces of the persons in the images. And that can be done by the automation of the unmasked people's images dataset. The dataset is trained using tensor flow/keras for providing the classifier which classifies the image. This research is valid on image and on live streams.

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Face Recognition from Surveillance Using Sequnetial CNN-Model

Cited by 2 publications

References 6 publications

Convolutional Neural Networks for Face Recognition: A Systematic Literature Review

Convolutional Neural Networks for Face Recognition: A Systematic Literature Review

Biometric Identification Using Face Mask DL and Open CV

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