Scientometric Analysis of Research Trends and Emerging Frontiers in Facial Age Estimation
DOI:
https://doi.org/10.63746/njtd.v23i2.4399Keywords:
Bibliometric mapping, computer vision, co-citation network, deep learning, facial age estimation, scientometric analysisAbstract
Facial age estimation (FAE) has become an important part of current computer vision systems and has applications in security, digital identity validation, healthcare, and human-computer interaction. Despite the high rate of methodological development, a holistic view of the research landscape, the patterns of collaboration, and emerging frontiers of FAE is still limited. This study presents a scientometric analysis of FAE research by using 378 publications retrieved from the Scopus database with the use of a keyword-based search strategy, by title, abstract and keywords, covering the time period 2008-2025. The dataset comprised journal articles and conference papers which were peer-reviewed. Following a structured screening process, a total of 328 documents were kept for detailed analysis using a software called VOSviewer. The publication trends, country contributions, institutional collaboration, author networks, and keyword co-occurrence and co-citation structures are analysed in the study. The result shows that China is leading the field in terms of number of publications, with 35.3% of all publications, followed by the United States of America and India. Keyword analysis shows that deep learning, facial recognition, and feature extraction are still important themes for research and that emerging trends lean towards attention mechanisms, domain generalisation, and multimodal learning. Beyond descriptive mapping, this research offers important information about the implications of the current research trends, such as dataset bias, model generalisation issues, and deployment constraints. The results provide a systematic basis for future studies and technical design of FAE.
References
Bao, Z.; Y. Luo; Z. Tan; J. Wan; X. Ma and Z. Lei. (2023). ‘Deep domain-invariant learning for facial age estimation’, Neurocomputing, 534, pp. 86–93. https://doi.org/10.1016/j.neucom.2023.02.037
Boedi, R.M.; S. Mânica and A. Franco. (2023). ‘Sixty years of research in dental age estimation: A bibliometric study’, Egyptian Journal of Forensic Sciences, 13(1). https://doi.org/10.1186/s41935-023-00360-3
Campanacho, V.; F. Alves-Cardoso. (2023). ‘Exploring adult age-at-death research in anthropology’, Forensic Sciences, 3(1), pp. 125–148.
Drobnyh, K.A.; A.N. Polovinkin. (2017). ‘Using supervised deep learning for human age estimation problem’, ISPRS Archives, XLII-2/W4. https://doi.org/10.5194/isprs-archives-xlii-2-w4-97-2017
Gao, Y.; Z. Zhang; X. Zhu and S. Ding. (2025). ‘Research progress on the integration of robot vision, computer vision and machine learning’, IJCRSET.
Geng, X.; K. Smith-Miles and Z.H. Zhou. (2008). ‘Facial age estimation by nonlinear aging pattern subspace’, Proceedings of ACM Multimedia. https://doi.org/10.1145/1459359.1459469
Government of India. (2018). National Strategy for Artificial Intelligence.
Han, H.; C. Otto; X. Liu and A.K. Jain. (2015). ‘Demographic estimation from face images: Human vs. machine performance’, IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(6). https://doi.org/10.1109/TPAMI.2014.2362759
Huerta, I.; C. Fernández; C. Segura; J. Hernando and A. Prati. (2015). ‘A deep analysis on age estimation’, Pattern Recognition Letters, 68. https://doi.org/10.1016/j.patrec.2015.06.006
Krizhevsky, A.; I. Sutskever and G.E. Hinton. (2012). ‘ImageNet classification with deep convolutional neural networks’, Advances in Neural Information Processing Systems.
Li, Y.; Z. Peng; D. Liang; H. Chang and Z. Cai. (2016). ‘Facial age estimation by using stacked feature composition and selection’, The Visual Computer, 32(12), pp. 1525–1536. https://doi.org/10.1007/s00371-015-1137-4
Liu, X. et al. (2015). ‘AgeNet: Deeply learned regressor and classifier for robust apparent age estimation’, in ICCV Workshop. https://doi.org/10.1109/ICCVW.2015.42
Liu, X.; M. Qiu; Z. Zhang and Y. Shi. (2025). ‘Enhancing facial age estimation with local and global multi-attention mechanisms’, Pattern Recognition Letters, 189, pp. 71–77. https://doi.org/10.1016/j.patrec.2025.01.005
Mongeon, P.; A. Paul-Hus. (2016). ‘The journal coverage of Web of Science and Scopus: A comparative analysis’.
Nane, G.F.; V. Larivière and R. Costas. (2017). ‘Predicting the age of researchers using bibliometric data’, Journal of Informetrics, 11(3), pp. 713–729. https://doi.org/10.1016/j.joi.2017.05.002
Panci, V.; L. Hackman. (2023). ‘Forensic age estimation of living individuals’, Journal of the Royal Anthropological Institute, 29(S2), pp. 50–74.
Ponraj, S.; K. Ramar; R. Sekar and A. Kasi. (2023). ‘Bibliometric analysis of research on dental age estimation’, Journal of Forensic Odonto-Stomatology.
Ricanek, K.; T. Tesafaye. (2006). ‘MORPH: A longitudinal image database of normal adult age-progression’, IEEE FGR.
Sada, A.Y.; M. Uthman and N.B. Gafai. (2025). Scientometric analysis of electrification planning for universal energy access: Trends, networks, and emerging research themes (1970–2025). Next Research, 2(4), 100832. https://doi.org/10.1016/j.nexres.2025.100832
Task Force. (2023). ‘Strengthening and democratizing the U.S. artificial intelligence innovation ecosystem’.
Tian, Q.; M. Cao and H. Sun. (2021). ‘Facial age estimation with bilateral relationships exploitation’, Neurocomputing, 444, pp. 158–169. https://doi.org/10.1016/j.neucom.2020.07.149
van Eck, N.J.; L. Waltman. (2010). ‘VOSviewer: A computer program for bibliometric mapping’, Scientometrics, 84(2), pp. 523–538.
Yu, Y.; Y. Li; Z. Zhang and Z. Gu. (2020). ‘A bibliometric analysis using VOSviewer of publications on COVID-19’, Annals of Translational Medicine, 8(13), p. 816. https://doi.org/10.21037/atm-20-4235
Zhao, Q.; Y. Li. (2025). ‘Facial age estimation by group-centric feature learning’, Expert Systems with Applications, 285, pp. 1–11. https://doi.org/10.1016/j.eswa.2025.128025
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Nigerian Journal of Technological Development
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
In accordance with the Copyright Act of 1976, which became effective January 1, 1978, the following statement signed by each author must accompany the manuscript submitted: "I, the undersigned author, transfer all copyright ownership of the manuscript referenced above to the Nigerian Journal of Technological Development, in the event the work is published. I warrant that the article is original, does not infringe upon any copyright or other proprietary right of any third party, is not under consideration by another journal, and has not been published previously. I have reviewed and approved the submitted version of the manuscript and agree to its publication in the Nigerian Journal of Technological Development." A copyright transfer form can be downloaded from the NJTD Website (http://njtd.com.ng/index.php/njtd). Author(s) will be consulted, whenever possible, regarding republication of material. All authors must have access to the data presented, and the authors and sponsor (if applicable) must agree to share original data with the editor if requested.
