论文标题
Fingergan：潜在指纹增强的指纹生成方案受限
FingerGAN: A Constrained Fingerprint Generation Scheme for Latent Fingerprint Enhancement
论文作者
论文摘要
潜在的指纹增强是潜在指纹识别的必要预处理步骤。大多数潜在的指纹增强方法试图恢复损坏的灰色山脊/山谷。在本文中，我们提出了一种新的方法，该方法将潜在的指纹增强为生成对抗网络（GAN）框架中的指纹生成问题。我们将提议的网络命名为Fingergan。它可以用与相应的地面真实实例相同的指纹（即增强的潜在指纹），该指纹实例无法与相应的地面真实实例相互区分。由于细节是指纹识别的主要特征，并且可以直接从指纹骨架图中检索细节，因此我们提供了一个整体框架，可以在直接优化细节信息的情况下执行潜在的指纹增强。这将有助于显着提高潜在的指纹识别性能。两个公共潜在指纹数据库的实验结果表明，我们的方法的表现大大优于艺术状态。这些代码可从\ url {https://github.com/hubyz/latentenhancement}提供非商业目的。
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}.
