Residual Feature Pyramid Network for Enhancement of Vascular Patterns
Ketan Kotwal Sebastien Marcel
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.