FingerGAN: A Constrained Fingerprint Generation Scheme for Latent Fingerprint Enhancement
This addresses the problem of improving latent fingerprint identification for forensic applications, representing a novel method for a known bottleneck.
The paper tackles latent fingerprint enhancement by formulating it as a constrained fingerprint generation problem using a GAN framework called FingerGAN, which optimizes minutia information directly and outperforms state-of-the-art methods on two public databases.
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}.