  Training fingerprint recognition models using synthetic data has recently
gained increased attention in the biometric community as it alleviates the
dependency on sensitive personal data. Existing approaches for fingerprint
generation are limited in their ability to generate diverse impressions of the
same finger, a key property for providing effective data for training
recognition models. To address this gap, we present FPGAN-Control, an identity
preserving image generation framework which enables control over the
fingerprint's image appearance (e.g., fingerprint type, acquisition device,
pressure level) of generated fingerprints. We introduce a novel appearance loss
that encourages disentanglement between the fingerprint's identity and
appearance properties. In our experiments, we used the publicly available NIST
SD302 (N2N) dataset for training the FPGAN-Control model. We demonstrate the
merits of FPGAN-Control, both quantitatively and qualitatively, in terms of
identity preservation level, degree of appearance control, and low
synthetic-to-real domain gap. Finally, training recognition models using only
synthetic datasets generated by FPGAN-Control lead to recognition accuracies
that are on par or even surpass models trained using real data. To the best of
our knowledge, this is the first work to demonstrate this.
