2025
doi: 10.12720/jait.16.4.458-477
Deep Learning in Biometric Authentication: Challenges, Recent Advancements, and Future Trends
Abdullah Alduhailan1,*
,
Nazhatul Hafizah Kamarudin1 ,
Siti Norul Huda Sheikh Abdullah1 ,
Aminu Dau2
- 1Centre for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia
- 2Department of Computer Science, Hassan Usman Katsina Polytechnic, Katsina, Nigeria
- Manuscript receivedOctober 24, 2024
- revisedNovember 12, 2024
- acceptedDecember 31, 2024
- publishedApril 9, 2025
Abstract
Biometric systems have significantly improved with the integration of machine learning, especially with deep learning methods. As technology advances, it presents both new security risks and chances for biometrics to be used in a variety of contexts. Due to their remarkable success, recently, deep learning-based biometric methods have seen a dramatic increase in research papers. Thus, to compile and analyze research publications in the field, this paper presents a systematic a Systematic Literature Review (SLR). The review adopts the Systematic Reviews and Meta-Analyses (PRISMA) approach for the collection, analysis, and reporting of findings from relevant research. For this study, 109 papers in all were chosen from top digital libraries using predetermined search criteria. The most popular deep learning approaches in these systems, important biometric authentication strategies, and commonly used assessment metrics are highlighted in the review. Finally, the main challenges with deep learning-based biometric authentication methods are outlined in the paper, along with possible future trends in the field.
Keywords
- biometrics
- authentication
- biometric recognition
- Deep Learning (DL)
- artificial neural network
How to Cite
Abdullah Alduhailan, Nazhatul Hafizah Kamarudin, Siti Norul Huda Sheikh Abdullah, and Aminu Dau, "Deep Learning in Biometric Authentication: Challenges, Recent Advancements, and Future Trends," Journal of Advances in Information Technology, vol. 16, no. 4, pp. 458-477, 2025. https://doi.org/10.12720/jait.16.4.458-477
Copyright & License
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
