Review

A Survey of Finger-vein Recognition using Deep Learning: Concepts, Challenges, and Opportunities

Volume: 13 Number: 3 September 30, 2025

A Survey of Finger-vein Recognition using Deep Learning: Concepts, Challenges, and Opportunities

Abstract

In recent years, convolutional neural networks have been frequently used for finger-vein biometrics. Various methodologies have been proposed to improve the recognition performance on available datasets. Deep learning-based approaches have a promising performance, and they have been an effective solution for feature learning. Nevertheless, some problems in the literature need to be solved, such as the lack of test protocol and comparability. In this study, a review of deep learning-based studies on finger-vein biometrics has been presented in two categories: identification and verification. This review contains 68 publications from reputable databases published between 2016 and 2025. The contents of the articles have been discussed in detail. The pros and cons of the proposed algorithms have been stated critically. The arising confusion due to the usage of the term recognition for identification and verification has been removed. The role of the experimental protocol and metrics in performance results on reviewed papers has been stated. The need for comparing the results against the existing results in the literature on the same finger-vein datasets using totally the same test protocol has been highlighted. Lastly, foreseen opportunities have been listed to draw the researcher's attention.

Keywords

References

  1. Jain, A. K., Ross, A., Prabhakar, S., An introduction to biometric recognition, IEEE Transactions on Circuits and Systems for Video Technology, 14, 1, 4-20, (2004).
  2. Daas, S., Yahi, A., Bakir, T., Sedhane, M., Boughazi, M., Bourennane, E. B., Multimodal biometric recognition systems using deep learning based on the finger vein and finger knuckle print fusion, IET Image Processing, 14, 15, 3859-3868, (2020).
  3. Qin, H., El-Yacoubi, M. A., Deep representation-based feature extraction and recovering for finger-vein verification, IEEE Transactions on Information Forensics and Security, 12, 8, 1816-1829, (2017).
  4. Krizhevsky, A., Sutskever, I., Hinton, G. E., Imagenet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, 25, (2012).
  5. Sidiropoulos, G. K., Kiratsa, P., Chatzipetrou, P., Papakostas, G. A., Feature extraction for fingervein-based identity recognition, Journal of Imaging, 7, 5, 89, (2021).
  6. Eglitis, T., Maiorana, E., Campisi, P., Influence of test protocols on biometric recognition performance estimation, 2021 International Conference of the Biometrics Special Interest Group (BIOSIG), 1-5, (2021).
  7. Marattukalam, F., Abdulla, W., Cole, D., Gulati, P., Deep learning-based wrist vascular biometric recognition, Sensors, 23, 6, 3132, (2023).
  8. Shaheed, K., Liu, H., Yang, G., Qureshi, I., Gou, J., Yin, Y., A systematic review of finger vein recognition techniques, Information, 9, 9, 213, (2018).

Details

Primary Language

English

Subjects

Deep Learning, Machine Vision

Journal Section

Review

Early Pub Date

September 30, 2025

Publication Date

September 30, 2025

Submission Date

April 9, 2025

Acceptance Date

June 8, 2025

Published in Issue

Year 2025 Volume: 13 Number: 3

APA
Kocakulak, M., Avcı, A., & Acır, N. (2025). A Survey of Finger-vein Recognition using Deep Learning: Concepts, Challenges, and Opportunities. Academic Platform Journal of Engineering and Smart Systems, 13(3), 71-93. https://doi.org/10.21541/apjess.1672743
AMA
1.Kocakulak M, Avcı A, Acır N. A Survey of Finger-vein Recognition using Deep Learning: Concepts, Challenges, and Opportunities. APJESS. 2025;13(3):71-93. doi:10.21541/apjess.1672743
Chicago
Kocakulak, Mustafa, Adem Avcı, and Nurettin Acır. 2025. “A Survey of Finger-Vein Recognition Using Deep Learning: Concepts, Challenges, and Opportunities”. Academic Platform Journal of Engineering and Smart Systems 13 (3): 71-93. https://doi.org/10.21541/apjess.1672743.
EndNote
Kocakulak M, Avcı A, Acır N (September 1, 2025) A Survey of Finger-vein Recognition using Deep Learning: Concepts, Challenges, and Opportunities. Academic Platform Journal of Engineering and Smart Systems 13 3 71–93.
IEEE
[1]M. Kocakulak, A. Avcı, and N. Acır, “A Survey of Finger-vein Recognition using Deep Learning: Concepts, Challenges, and Opportunities”, APJESS, vol. 13, no. 3, pp. 71–93, Sept. 2025, doi: 10.21541/apjess.1672743.
ISNAD
Kocakulak, Mustafa - Avcı, Adem - Acır, Nurettin. “A Survey of Finger-Vein Recognition Using Deep Learning: Concepts, Challenges, and Opportunities”. Academic Platform Journal of Engineering and Smart Systems 13/3 (September 1, 2025): 71-93. https://doi.org/10.21541/apjess.1672743.
JAMA
1.Kocakulak M, Avcı A, Acır N. A Survey of Finger-vein Recognition using Deep Learning: Concepts, Challenges, and Opportunities. APJESS. 2025;13:71–93.
MLA
Kocakulak, Mustafa, et al. “A Survey of Finger-Vein Recognition Using Deep Learning: Concepts, Challenges, and Opportunities”. Academic Platform Journal of Engineering and Smart Systems, vol. 13, no. 3, Sept. 2025, pp. 71-93, doi:10.21541/apjess.1672743.
Vancouver
1.Mustafa Kocakulak, Adem Avcı, Nurettin Acır. A Survey of Finger-vein Recognition using Deep Learning: Concepts, Challenges, and Opportunities. APJESS. 2025 Sep. 1;13(3):71-93. doi:10.21541/apjess.1672743

Academic Platform Journal of Engineering and Smart Systems