Study of Full-View Finger Vein Biometrics on Redundancy Analysis and Dynamic Feature Extraction
| Type of publication: | Journal paper |
| Citation: | Huang_IEEE-TIFS_2025 |
| Publication status: | Accepted |
| Journal: | IEEE Transactions on Information Forensics and Security |
| Year: | 2025 |
| ISSN: | 1556-6021 |
| URL: | https://ieeexplore.ieee.org/do... |
| DOI: | 10.1109/TIFS.2025.3630891 |
| Abstract: | As a biometric trait drawing increasing attention, finger vein (FV) has been studied from many perspectives. One promising new direction in FV biometrics research is full-view FV biometrics, where multiple images, covering the entire surface of the presented finger, are captured. Full-view FV biometrics presents two main problems: increased computational load, and low performance-to-cost ratio for some views/regions. Both problems are related to the inherent redundancy in vascular information available in full-view FV images. In this work, we address this redundancy issue in full-view FV biometrics. Firstly, we propose a straightforward FV redundancy analysis (FVRA) method for quantifying the information redundancy in FV images. Our analysis shows that the redundancy ratio of full-view FV images is up to 83%-87%. Then, we propose a novel feature extraction model, named FV dynamic Transformer (FDT), whose architecture is configured based on the redundancy analysis results. The FDT focuses on both local (single-view) information as well as global (full view) information at different processing stages. Both stages provide the advantage of de-redundancy and noise avoidance. Additionally, the end-to-end architecture simplifies the full-view FV biometrics pipeline by enabling the direct, simultaneous processing of multiple input images, thus consolidating multiple steps into one. A series of rigorous experiments is conducted to evaluate the effectiveness of the proposed methods. Experimental results show that the proposed FDT achieves state of the art authentication performance on the MFFV-N dataset, yielding an EER of 0.97% on the development set and an HTER of 1.84% on the test set under the balanced protocol and EER criterion. The cross-domain generalization capability of FDT is also demonstrated on the LFMB-3DFB dataset, where it achieves an EER of 7.24% and an HTER of 7.34% under the same protocol and criterion. Code for the proposed methods can be access via: https://github.com/SCUT-BIP-Lab/FDT. |
| Main Research Program: | AI for Everyone |
| Keywords: | authentication, Biometrics, Cameras, Convolutional Neural Networks, dynamic Transformer, Feature extraction, Finger vein, Fingers, full-view, Imaging, redundancy, Taxonomy, transformers, Vein recognition |
| Projects: |
Idiap Biometrics Center |
| Authors: | |
| Added by: | [UNK] |
| Total mark: | 0 |
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