  We study the finger vein (FV) sensor model identification task using a deep
learning approach. So far, for this biometric modality, only correlation-based
PRNU and texture descriptor-based methods have been applied. We employ five
prominent CNN architectures covering a wide range of CNN family models,
including VGG16, ResNet, and the Xception model. In addition, a novel
architecture termed FV2021 is proposed in this work, which excels by its
compactness and a low number of parameters to be trained. Original samples, as
well as the region of interest data from eight publicly accessible FV datasets,
are used in experimentation. An excellent sensor identification AUC-ROC score
of 1.0 for patches of uncropped samples and 0.9997 for ROI samples have been
achieved. The comparison with former methods shows that the CNN-based approach
is superior and improved the results.
