  Finger vein authentication, recognized for its high security and specificity,
has become a focal point in biometric research. Traditional methods
predominantly concentrate on vein feature extraction for discriminative
modeling, with a limited exploration of generative approaches. Suffering from
verification failure, existing methods often fail to obtain authentic vein
patterns by segmentation. To fill this gap, we introduce DiffVein, a unified
diffusion model-based framework which simultaneously addresses vein
segmentation and authentication tasks. DiffVein is composed of two dedicated
branches: one for segmentation and the other for denoising. For better feature
interaction between these two branches, we introduce two specialized modules to
improve their collective performance. The first, a mask condition module,
incorporates the semantic information of vein patterns from the segmentation
branch into the denoising process. Additionally, we also propose a Semantic
Difference Transformer (SD-Former), which employs Fourier-space self-attention
and cross-attention modules to extract category embedding before feeding it to
the segmentation task. In this way, our framework allows for a dynamic
interplay between diffusion and segmentation embeddings, thus vein segmentation
and authentication tasks can inform and enhance each other in the joint
training. To further optimize our model, we introduce a Fourier-space
Structural Similarity (FourierSIM) loss function, which is tailored to improve
the denoising network's learning efficacy. Extensive experiments on the USM and
THU-MVFV3V datasets substantiates DiffVein's superior performance, setting new
benchmarks in both vein segmentation and authentication tasks.
