  The accuracy of finger vein recognition systems gets degraded due to low and
uneven contrast between veins and surroundings, often resulting in poor
detection of vein patterns. We propose a finger-vein enhancement technique,
ResFPN (Residual Feature Pyramid Network), as a generic preprocessing method
agnostic to the recognition pipeline. A bottom-up pyramidal architecture using
the novel Structure Detection block (SDBlock) facilitates extraction of veins
of varied widths. Using a feature aggregation module (FAM), we combine these
vein-structures, and train the proposed ResFPN for detection of veins across
scales. With enhanced presentations, our experiments indicate a reduction upto
5% in the average recognition errors for commonly used recognition pipeline
over two publicly available datasets. These improvements are persistent even in
cross-dataset scenario where the dataset used to train the ResFPN is different
from the one used for recognition.
