  Performance of fingerprint recognition algorithms substantially rely on fine
features extracted from fingerprints. Apart from minutiae and ridge patterns,
pore features have proven to be usable for fingerprint recognition. Although
features from minutiae and ridge patterns are quite attainable from
low-resolution images, using pore features is practical only if the fingerprint
image is of high resolution which necessitates a model that enhances the image
quality of the conventional 500 ppi legacy fingerprints preserving the fine
details. To find a solution for recovering pore information from low-resolution
fingerprints, we adopt a joint learning-based approach that combines both
super-resolution and pore detection networks. Our modified single image
Super-Resolution Generative Adversarial Network (SRGAN) framework helps to
reliably reconstruct high-resolution fingerprint samples from low-resolution
ones assisting the pore detection network to identify pores with a high
accuracy. The network jointly learns a distinctive feature representation from
a real low-resolution fingerprint sample and successfully synthesizes a
high-resolution sample from it. To add discriminative information and
uniqueness for all the subjects, we have integrated features extracted from a
deep fingerprint verifier with the SRGAN quality discriminator. We also add
ridge reconstruction loss, utilizing ridge patterns to make the best use of
extracted features. Our proposed method solves the recognition problem by
improving the quality of fingerprint images. High recognition accuracy of the
synthesized samples that is close to the accuracy achieved using the original
high-resolution images validate the effectiveness of our proposed model.
