  Today's legal restrictions that protect the privacy of biometric data are
hampering fingerprint recognition researches. For instance, all high-resolution
fingerprint databases ceased to be publicly available. To address this problem,
we present a novel hybrid approach to synthesize realistic, high-resolution
fingerprints. First, we improved Anguli, a handcrafted fingerprint generator,
to obtain dynamic ridge maps with sweat pores and scratches. Then, we trained a
CycleGAN to transform these maps into realistic fingerprints. Unlike other
CNN-based works, we can generate several images for the same identity. We used
our approach to create a synthetic database with 7400 images in an attempt to
propel further studies in this field without raising legal issues. We included
sweat pore annotations in 740 images to encourage research developments in pore
detection. In our experiments, we employed two fingerprint matching approaches
to confirm that real and synthetic databases have similar performance. We
conducted a human perception analysis where sixty volunteers could hardly
differ between real and synthesized fingerprints. Given that we also favorably
compare our results with the most advanced works in the literature, our
experimentation suggests that our approach is the new state-of-the-art.
