  Fingerprint recognition has been utilized for cellphone authentication,
airport security and beyond. Many different features and algorithms have been
proposed to improve fingerprint recognition. In this paper, we propose an
end-to-end deep learning framework for fingerprint recognition using
convolutional neural networks (CNNs) which can jointly learn the feature
representation and perform recognition. We train our model on a large-scale
fingerprint recognition dataset, and improve over previous approaches in terms
of accuracy. Our proposed model is able to achieve a very high recognition
accuracy on a well-known fingerprint dataset. We believe this framework can be
widely used for biometrics recognition tasks, making more scalable and accurate
systems possible. We have also used a visualization technique to highlight the
important areas in an input fingerprint image, that mostly impact the
recognition results.
