Research on the Accuracy and Efficiency of Near-infrared Face Recognition

Yan, Minrui

doi:10.1109/aeeca55500.2022.9919035

Cited by 1 publication

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First, those that focus on new face recognition approaches and techniques, examples of those are (Moghimi and Grailu, 2021;Han, 2021;Ahmed et al, 2021;Ben Fredj et al, 2021b;Tabassum et al, 2022;Ahmed et al, 2022;Yan, 2022). Authors in (Moghimi and Grailu, 2021) present a method based on image segmentation, statistical features, and CNN.…”

Section: 60%mentioning

confidence: 99%

“…(Ahmed et al, 2022) discusses the momentum gradient dependent on the CNNs organization's strong point and introduce a new methodology to detect evenness in the data set of faces. Finally, (Yan, 2022) design an effective near-infrared face recognition based on CNN with different image sizes as input.…”

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%

See 2 more Smart Citations

Convolutional Neural Networks for Face Recognition: A Systematic Literature Review

Barcic¹,

Grd²,

Tomičić³

2023

Preprint

View full text Add to dashboard Cite

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.

show abstract