Finger Vein Identification Using Pretrained Feature Matching Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0197753" target="_blank" >RIV/00216305:26230/26:0197753 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/11386897" target="_blank" >https://ieeexplore.ieee.org/document/11386897</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACCESS.2026.3662722" target="_blank" >10.1109/ACCESS.2026.3662722</a>
Alternative languages
Result language
angličtina
Original language name
Finger Vein Identification Using Pretrained Feature Matching Networks
Original language description
This work investigates the feasibility of using pretrained, feature-based matching neural networks for finger-vein–based person identification without retraining on biometric datasets. The proposed solution compares two finger-vein images by extracting point correspondences and computing a similarity measure based on the number and spatial distribution of matches. To enforce geometric consistency, we apply homography-based verification using the matched points. We evaluate the capability of several pretrained neural network models (SuperGlue, GlueStick, ASpanFormer, LoFTR, and SGM-Net) to verify and identify individuals based on images of finger veins on three publicly available datasets (SDUMLA, MMCBNU, and FV-USM) without additional training or fine-tuning. Experiments cover verification via pairwise comparisons and open-set identification, using a single parameter setting across all datasets to assess robustness. Verification achieves an accuracy above 99%. In the open-set identification setting, the best result yields an equal error rate (EER) below 3.5%. These results indicate that general-purpose matching networks can transfer effectively to finger-vein recognition without biometric-specific retraining.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2026
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Access
ISSN
2169-3536
e-ISSN
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Volume of the periodical
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Issue of the periodical within the volume
VOLUME 14, 2026
Country of publishing house
US - UNITED STATES
Number of pages
10
Pages from-to
23814-23823
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-105030037417