  The use of deep learning methods to extract vascular biometric patterns from
the palm surface has been of interest among researchers in recent years. In
many biometric recognition tasks, there is a limit in the number of training
samples. This is because of limited vein biometric databases being available
for research. This restricts the application of deep learning methods to design
algorithms that can effectively identify or authenticate people for vein
recognition. This paper proposes an architecture using Siamese neural network
structure for few shot palm vein identification. The proposed network uses
images from both the palms and consists of two sub-nets that share weights to
identify a person. The architecture performance was tested on the HK PolyU
multi spectral palm vein database with limited samples. The results suggest
that the method is effective since it has 91.9% precision, 91.1% recall, 92.2%
specificity, 91.5%, F1-Score, and 90.5% accuracy values.
