FingerGAN: A Constrained Fingerprint Generation Scheme for Latent Fingerprint Enhancement

TL;DR

FingerGAN introduces a novel GAN-based approach for latent fingerprint enhancement that emphasizes minutia and skeleton map consistency, significantly improving identification accuracy over existing methods.

Contribution

This paper presents FingerGAN, a new constrained GAN framework that enhances latent fingerprints by focusing on minutia-preserving skeleton and orientation maps, a novel approach in fingerprint enhancement.

Findings

Outperforms state-of-the-art methods on public datasets

Effectively preserves minutia for better recognition

Enhances latent fingerprint images significantly

Abstract

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}.