Bibliometrical Analysis in Multimedia Jurnal Face Recognition

Authors

  • Muhammad Dziya Diayuddin Universitas Majalengka
  • Aldi Hidayatullah University Majalengka

DOI:

https://doi.org/10.58812/wsis.v1i11.336

Keywords:

Bibliometrics, Multimedia, Face Recognition, Situation Analysis, Co-Quotation Analyses, Bibliographic Analysis

Abstract

The objective of this research is to bibliometrically analyze multimedia journals on the subject of “face recognition” with a focus on developments and related research trends over a certain period of time. The research uses bibliometric methodologies to identify and analyze scientific publications related to face recognition in the multimedia world, including trends in research, major contributors, the most frequently quoted journal, and how research topics evolve over time. These findings provide an important understanding of how multimedia face reconnaissance evolves and develops, and provide a solid foundation for further research in this field. Thus, this research can be an important reference for researchers, practitioners, and decision makers in the fields of artificial intelligence, facial recognition, multimedia technology in general.

References

J. Al-Nabulsi, N. Turab, H. A. Owida, B. Al-Naami, R. De Fazio, and P. Visconti, “IoT Solutions and AI-Based Frameworks for Masked-Face and Face Recognition to Fight the COVID-19 Pandemic,” Sensors, vol. 23, no. 16, p. 7193, 2023.

S. Fang, J. Yang, N. Liu, W. Sun, and T. Zhao, “Face recognition using weber local circle gradient pattern method,” Multimed. Tools Appl., vol. 77, pp. 2807–2822, 2018.

K. G. Shanthi and P. Sivalakshmi, “Smart drone with real time face recognition,” Mater. Today Proc., vol. 80, pp. 3212–3215, 2023.

S. Sharma and V. Kumar, “Voxel-based 3D face reconstruction and its application to face recognition using sequential deep learning,” Multimed. Tools Appl., vol. 79, pp. 17303–17330, 2020.

Y. Zhang, C. Hu, and X. Lu, “Face recognition under varying illumination based on singular value decomposition and retina modeling,” Multimed. Tools Appl., vol. 77, pp. 28355–28374, 2018.

L. Zhou, H. Wang, S. Lin, S. Hao, and Z.-M. Lu, “Face recognition based on local binary pattern and improved pairwise-constrained multiple metric learning,” Multimed. Tools Appl., vol. 79, pp. 675–691, 2020.

A. Atmaja, S. Setyawan, L. Setia, S. Yulianto, B. Winarno, and T. Lestariningsih, “Face Recognition System using Micro Unmanned Aerial Vehicle,” J. Phys. Conf. Ser., vol. 1845, p. 12043, Mar. 2021, doi: 10.1088/1742-6596/1845/1/012043.

A. M. Zayeef and R. J. Chakma, “Face Recognition-Based Automated Attendance System for Educational Institutions Utilizing Machine Learning,” in Information and Communication Technology for Competitive Strategies (ICTCS 2022) Intelligent Strategies for ICT, Springer, 2023, pp. 325–333.

O. P. Gupta, A. P. Agarwal, and O. Pal, “A study on Evolution of Facial Recognition Technology,” in 2023 International Conference on Disruptive Technologies (ICDT), IEEE, 2023, pp. 769–775.

L. He, L. He, and L. Peng, “CFormerFaceNet: Efficient Lightweight Network Merging a CNN and Transformer for Face Recognition,” Appl. Sci., vol. 13, no. 11, p. 6506, 2023.

H. Benradi, A. Chater, and A. Lasfar, “A hybrid approach for face recognition using a convolutional neural network combined with feature extraction techniques,” IAES Int. J. Artif. Intell., vol. 12, no. 2, p. 627, 2023.

P. Du, X. Zheng, L. Liu, and H. Ma, “LC-GAN: Improving Adversarial Robustness of Face Recognition Systems on Edge Devices,” IEEE Internet Things J., vol. 10, no. 9, pp. 8172–8184, 2022.

R. Talib, “A survey of Face detection and Recognition system,” Iraqi J. Intell. Comput. Informatics, vol. 2, no. 1, pp. 44–57, 2023.

S. Soharika and N. P. G. Bhavani, “Identify Facial Micro Expression Using Support Vector Machine Compared with Artificial Neural Network to Improve Recall Parameter,” in 2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF), IEEE, 2023, pp. 1–5.

M. Kolla and A. Savadamuthu, “The Impact of Racial Distribution in Training Data on Face Recognition Bias: A Closer Look,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023, pp. 313–322.

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Published

2023-11-23

How to Cite

Diayuddin, M. D., & Hidayatullah, A. (2023). Bibliometrical Analysis in Multimedia Jurnal Face Recognition. West Science Interdisciplinary Studies, 1(11), 1128–1139. https://doi.org/10.58812/wsis.v1i11.336

Issue

Vol. 1 No. 11 (2023): West Science Interdisciplinary Studies

Section

Articles

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Copyright (c) 2023 Muhammad Dziya Diayuddin, Aldi Hidayatullah

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