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Latent fingerprint enhancement is an essential pre-processing step for latentfingerprint identification. Most latent fingerprint enhancement methods try torestore corrupted gray ridges/valleys. In this paper, we propose a new methodthat formulates the latent fingerprint enhancement as a constrained fingerprintgeneration problem within a generative adversarial network (GAN) framework. Wename the proposed network as FingerGAN. It can enforce its generatedfingerprint (i.e, enhanced latent fingerprint) indistinguishable from thecorresponding ground-truth instance in terms of the fingerprint skeleton mapweighted by minutia locations and the orientation field regularized by theFOMFE model. Because minutia is the primary feature for fingerprint recognitionand minutia can be retrieved directly from the fingerprint skeleton map, weoffer a holistic framework which can perform latent fingerprint enhancement inthe context of directly optimizing minutia information. This will help improvelatent fingerprint identification performance significantly. Experimentalresults on two public latent fingerprint databases demonstrate that our methodoutperforms the state of the arts significantly. The codes will be availablefor non-commercial purposes from\url{https://github.com/HubYZ/LatentEnhancement}.

FingerGAN: A Constrained Fingerprint Generation Scheme for Latent Fingerprint Enhancement