Facial Recognition Technology: Methods and Challenges
Research Article
Open Access
CC BY

Facial Recognition Technology: Methods and Challenges

Yuyan Wu 1*
1 College of Computer Science, Beijing University of Technology, Beijing, China
*Corresponding author: wuyuyan@emails.bjut.edu.cn
Published on 5 November 2025
Volume Cover
ACE Vol.204
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-80590-517-2
ISBN (Online): 978-1-80590-518-9
Download Cover

Abstract

With the development and progress of science and technology, facial recognition has become an important research direction in the field of computer science and has been widely applied in areas such as identity verification and security. This paper systematically reviews traditional and emerging face recognition methods, covering classic algorithms such as two-dimensional principal component analysis (2DPCA), as well as the rapidly developing deep learning techniques in recent years. Among them, the face recognition method based on deep convolutional neural networks has significantly improved the recognition accuracy and robustness compared with the original two-dimensional principal component analysis algorithm. For complex scenarios such as occlusion, illumination changes and posture differences,this paper introduces an improved method based on attention mechanism, generative adversarial network, and 3D point features. These approaches significantly enhance recognition accuracy in challenging scenarios, particularly in occluded settings such as wearing masks, and under special conditions including weak lighting.In addition, this paper summarizes the bottlenecks in the current research status and points out the future development directions and trends.

Keywords:

Facial Recognition, Two-Dimensional Principal Component Analysis (2DPCA), Deep Learning, Convolutional Neural Networks (CNN), Occlusion and Illumination Variation

View PDF
Wu,Y. (2025). Facial Recognition Technology: Methods and Challenges. Applied and Computational Engineering,204,29-33.

References

[1]. Sun, Y. N. (2013). Face recognition method based on 2DPCA (Master’s thesis). Xidian University.

[2]. Fu, X. T. (2019). Research on face recognition technology based on deep learning. Communications World, 26(02), 299–300.

[3]. Zhang, M. Z. (2025). Application research of face recognition technology based on deep learning in smart home. Information & Computer, 37(13), 5–7.

[4]. Gao, X. Q. (2025). Application research of deep learning algorithm in face recognition system. Software, 46(04), 47–49.

[5]. Su, X. P., Sun, D. D., Li, Y. H., & others. (2025). Face recognition algorithm under mask occlusion based on joint multi-view features. Journal of Northwest University (Natural Science Edition), 55(02), 286–296.

[6]. Wang, L. J., Wang, J. H., & Wei, Y. F. (2025). 3D face recognition method under occlusion based on ridge point features. Computer and Digital Engineering, 53(01), 103–107, 163.

[7]. Wang, L. Y., Li, Y., Guo, L., & others. (2025). Multi-pose facial expression recognition method under varying illumination. Modern Electronic Technology, 48(14), 154–158.

[8]. Lin, S. N., Zhao, R., & Zhang, W. (2025). Lightweight YOLOv5 detection algorithm for facial expression recognition. Computer Engineering and Design, 46(07), 2028–2036.

[9]. Kan, J. X., & Zhu, Z. (2024). Research on face recognition method based on YOLOv5-face. Journal of Jinling Institute of Technology, 40(04), 8–14, 54.

[10]. Song, C. Q. (2023). Research on face recognition detection method based on YOLOv5 algorithm. Computer Era, (07), 15–19.

References

[1]. Sun, Y. N. (2013). Face recognition method based on 2DPCA (Master’s thesis). Xidian University.

[2]. Fu, X. T. (2019). Research on face recognition technology based on deep learning. Communications World, 26(02), 299–300.

[3]. Zhang, M. Z. (2025). Application research of face recognition technology based on deep learning in smart home. Information & Computer, 37(13), 5–7.

[4]. Gao, X. Q. (2025). Application research of deep learning algorithm in face recognition system. Software, 46(04), 47–49.

[5]. Su, X. P., Sun, D. D., Li, Y. H., & others. (2025). Face recognition algorithm under mask occlusion based on joint multi-view features. Journal of Northwest University (Natural Science Edition), 55(02), 286–296.

[6]. Wang, L. J., Wang, J. H., & Wei, Y. F. (2025). 3D face recognition method under occlusion based on ridge point features. Computer and Digital Engineering, 53(01), 103–107, 163.

[7]. Wang, L. Y., Li, Y., Guo, L., & others. (2025). Multi-pose facial expression recognition method under varying illumination. Modern Electronic Technology, 48(14), 154–158.

[8]. Lin, S. N., Zhao, R., & Zhang, W. (2025). Lightweight YOLOv5 detection algorithm for facial expression recognition. Computer Engineering and Design, 46(07), 2028–2036.

[9]. Kan, J. X., & Zhu, Z. (2024). Research on face recognition method based on YOLOv5-face. Journal of Jinling Institute of Technology, 40(04), 8–14, 54.

[10]. Song, C. Q. (2023). Research on face recognition detection method based on YOLOv5 algorithm. Computer Era, (07), 15–19.

Cite this article

Wu,Y. (2025). Facial Recognition Technology: Methods and Challenges. Applied and Computational Engineering,204,29-33.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MLA 2025 Symposium: Intelligent Systems and Automation: AI Models, IoT, and Robotic Algorithms

ISBN: 978-1-80590-517-2(Print) / 978-1-80590-518-9(Online)
Editor: Hisham AbouGrad
Conference website: https://www.confmla.org/london.html
Conference date: 12 November 2025
Series: Applied and Computational Engineering
Volume number: Vol.204
ISSN: 2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:

1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.

2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.

3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

For authors
For editors
For reviewers

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.