Finger Vein Identification Using Pretrained Feature Matching Networks

Finger Vein Identification Using Pretrained Feature Matching Networks

Článek Scopus

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.

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.

finger vein, biometry, recognition, identification, neural networks, homography

finger vein, biometry, recognition, identification, neural networks, homography

RYDLO, Š.; ORSÁG, F.; GOLDMANN, T.; KOLÁŘ, D.

2026

18.02.2026

IEEE (Institute of Electrical and Electronics Engineers)

IEEE Access

VOLUME 14, 2026

Spojené státy americké

23814

23823

10

https://ieeexplore.ieee.org/document/11386897