  Authentication and identification methods based on human fingerprints are
ubiquitous in several systems ranging from government organizations to consumer
products. The performance and reliability of such systems directly rely on the
volume of data on which they have been verified. Unfortunately, a large volume
of fingerprint databases is not publicly available due to many privacy and
security concerns.
  In this paper, we introduce a new approach to automatically generate
high-fidelity synthetic fingerprints at scale. Our approach relies on (i)
Generative Adversarial Networks to estimate the probability distribution of
human fingerprints and (ii) Super-Resolution methods to synthesize fine-grained
textures. We rigorously test our system and show that our methodology is the
first to generate fingerprints that are computationally indistinguishable from
real ones, a task that prior art could not accomplish.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning