  Latent fingerprint enhancement is an essential pre-processing step for latent
fingerprint identification. Most latent fingerprint enhancement methods try to
restore corrupted gray ridges/valleys. In this paper, we propose a new method
that formulates the latent fingerprint enhancement as a constrained fingerprint
generation problem within a generative adversarial network (GAN) framework. We
name the proposed network as FingerGAN. It can enforce its generated
fingerprint (i.e, enhanced latent fingerprint) indistinguishable from the
corresponding ground-truth instance in terms of the fingerprint skeleton map
weighted by minutia locations and the orientation field regularized by the
FOMFE model. Because minutia is the primary feature for fingerprint recognition
and minutia can be retrieved directly from the fingerprint skeleton map, we
offer a holistic framework which can perform latent fingerprint enhancement in
the context of directly optimizing minutia information. This will help improve
latent fingerprint identification performance significantly. Experimental
results on two public latent fingerprint databases demonstrate that our method
outperforms the state of the arts significantly. The codes will be available
for non-commercial purposes from
\url{https://github.com/HubYZ/LatentEnhancement}.
