  Deep learning-based models have been shown to improve the accuracy of
fingerprint recognition. While these algorithms show exceptional performance,
they require large-scale fingerprint datasets for training and evaluation. In
this work, we propose a novel fingerprint synthesis and reconstruction
framework based on the StyleGan2 architecture, to address the privacy issues
related to the acquisition of such large-scale datasets. We also derive a
computational approach to modify the attributes of the generated fingerprint
while preserving their identity. This allows synthesizing multiple different
fingerprint images per finger. In particular, we introduce the SynFing
synthetic fingerprints dataset consisting of 100K image pairs, each pair
corresponding to the same identity. The proposed framework was experimentally
shown to outperform contemporary state-of-the-art approaches for both
fingerprint synthesis and reconstruction. It significantly improved the realism
of the generated fingerprints, both visually and in terms of their ability to
spoof fingerprint-based verification systems. The code and fingerprints dataset
are publicly available: https://github.com/rafaelbou/fingerprint_generator.
