Harris Hawks Optimization Method based on Convolutional Neural Network for Face Recognition Systems

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

doi:10.1109/hora55278.2022.9799955

Cited by 5 publications

(4 citation statements)

References 26 publications

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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%

“…(Tabassum et al, 2022) combine Discrete Wavelet Transform (DWT) with four different algorithms: error vector of PCA, eigenvector of PCA, eigenvector of LDA and CNN. (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%

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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%

Section: 60%mentioning

confidence: 99%

Convolutional Neural Networks for Face Recognition: A Systematic Literature Review

Barcic¹,

Grd²,

Tomičić³

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The linear classifier of a 2D space is defined by the function of W T x + B = 0, in which W is the hyperplane direction and B is its exact position [4], [58]. Items outside of the hyperplanes represent two separate categories, and the coordinates belonging to the hyperplane are known as support vectors.…”

Section: Support Vector Machinementioning

confidence: 99%

“…Items outside of the hyperplanes represent two separate categories, and the coordinates belonging to the hyperplane are known as support vectors. SVM is robust and has optimal accuracy values, although it is highly complex and requires extensive memory usage for large scale tasks [4], [58]. Fig.…”

Section: Support Vector Machinementioning

confidence: 99%

Automated Semantic Annotation Deploying Machine Learning Approaches: A Systematic Review

Chang,

Sangodiah

2023

mendel

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Semantic Web is the vision to make Internet data machine-readable to achieve information retrieval with higher granularity and personalisation. Semantic annotation is the process that binds machine-understandable descriptions into Web resources such as text and images. Hence, the success of Semantic Web dependson the wide availability of semantically annotated Web resources. However, there remains a huge amount of unannotated Web resources due to the limited annotation capability available. In order to address this, machine learning approaches have been used to improve the automation process. This Systematic Review aims to summarise the existing state-of-the-art literature to answer five Research Questions focusing on machine learning driven semantic annotation automation. The analysis of 40 selected primary studies reveals that the use of unitary and combination of machine learning algorithms are both the current directions. SupportVector Machine (SVM) is the most-used algorithm, and supervised learning is the predominant machine learning type. Both semi-automated and fully automated annotation are almost nearly achieved. Meanwhile, text is the most annotated Web resource; and the availability of third-party annotation tools is in-line with this. While Precision, Recall, F-Measure and Accuracy are the most deployed quality metrics, not all the studies measured the quality of the annotated results. In the future, standardising quality measures is the direction for research.

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The Effectiveness of Adaptive Learning Methods in Mathematics Compared to Flipped Learning analyzed by ANN and IoT

Rahebi

İbrahim

2023

Wireless Pers Commun

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Harris Hawks Optimization Method based on Convolutional Neural Network for Face Recognition Systems

Cited by 5 publications

References 26 publications

Convolutional Neural Networks for Face Recognition: A Systematic Literature Review

Convolutional Neural Networks for Face Recognition: A Systematic Literature Review

Automated Semantic Annotation Deploying Machine Learning Approaches: A Systematic Review

The Effectiveness of Adaptive Learning Methods in Mathematics Compared to Flipped Learning analyzed by ANN and IoT

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