Finger Vein Identification Using Pretrained Feature Matching Networks

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Finger Vein Identification Using Pretrained Feature Matching Networks

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Finger Vein Identification Using Pretrained Feature Matching Networks

Popis výsledku anglicky

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.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2026

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

—

Svazek periodika

—

Číslo periodika v rámci svazku

VOLUME 14, 2026

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

10

Strana od-do

23814-23823

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-105030037417