Finger vein recognition has evolved into a major biometric trait in recent years. Despite various improvements in recognition accuracy and usability, finger vein recognition is still far from being perfect as it suffers from low-contrast images and other imaging artefacts. Three-dimensional or multi-perspective finger vein recognition technology provides a way to tackle some of the current problems, especially finger misplacement and rotations. In this work we present a novel multi-perspective finger vein capturing device that is based on mirrors, in contrast to most of the existing devices, which are usually based on multiple cameras. This new device only uses a single camera, a single illumination module and several mirrors to capture the finger at different rotational angles. To derive the need for this new device, we at first summarise the state of the art in multi-perspective finger vein recognition and identify the potential problems and shortcomings of the current devices.

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.