Face Recognition System using Histograms of Oriented Gradients and Convolutional Neural Network based on with Particle Swarm Optimization

Ahmed, Sulayman; Frikha, Mounir; Hussein, Taha Darwassh Hanawy; Rahebi, Javad

doi:10.1109/icecce52056.2021.9514139

Cited by 7 publications

(2 citation statements)

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“…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%

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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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Section: 60%mentioning

confidence: 99%

“…They add a convolution layer of the hidden state saved as a parameter and require a small resource capacity. (Ahmed et al, 2021) present a framework to be utilized for various purposes. They focus on Histograms of Oriented Gradients (HOG) dependent on the strong point of CNN organization.…”

Section: 60%mentioning

confidence: 99%