  Designing an end-to-end deep learning network to match the biometric features
with limited training samples is an extremely challenging task. To address this
problem, we propose a new way to design an end-to-end deep CNN framework i.e.,
PVSNet that works in two major steps: first, an encoder-decoder network is used
to learn generative domain-specific features followed by a Siamese network in
which convolutional layers are pre-trained in an unsupervised fashion as an
autoencoder. The proposed model is trained via triplet loss function that is
adjusted for learning feature embeddings in a way that minimizes the distance
between embedding-pairs from the same subject and maximizes the distance with
those from different subjects, with a margin. In particular, a triplet Siamese
matching network using an adaptive margin based hard negative mining has been
suggested. The hyper-parameters associated with the training strategy, like the
adaptive margin, have been tuned to make the learning more effective on
biometric datasets. In extensive experimentation, the proposed network
outperforms most of the existing deep learning solutions on three type of
typical vein datasets which clearly demonstrates the effectiveness of our
proposed method.
