Latent fingerprint matching is a very important but unsolved problem. As a key step of fingerprint matching, fingerprint registration has a great impact on the recognition performance. Existing latent fingerprint registration approaches are mainly based on establishing correspondences between minutiae, and hence will certainly fail when there are no sufficient number of extracted minutiae due to small fingerprint area or poor image quality. Minutiae extraction has become the bottleneck of latent fingerprint registration. In this paper, we propose a non-minutia latent fingerprint registration method which estimates the spatial transformation between a pair of fingerprints through a dense fingerprint patch alignment and matching procedure. Given a pair of fingerprints to match, we bypass the minutiae extraction step and take uniformly sampled points as key points. Then the proposed patch alignment and matching algorithm compares all pairs of sampling points and produces their similarities along with alignment parameters. Finally, a set of consistent correspondences are found by spectral clustering. Extensive experiments on NIST27 database and MOLF database show that the proposed method achieves the state-of-the-art registration performance, especially under challenging conditions. Code is made publicly available at: https://github.com/Gus233/Latent-Fingerprint-Registration .

中文翻译：

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通过匹配密集采样点的潜在指纹注册

潜在的指纹匹配是一个非常重要但尚未解决的问题。指纹注册是指纹匹配的关键步骤，对识别性能有很大影响。现有的潜在指纹注册方法主要基于细节之间的建立对应关系，因此，由于指纹面积小或图像质量较差，当提取的细节数量不足时，肯定会失败。细节提取已成为潜在指纹注册的瓶颈。在本文中，我们提出了一种非细节潜伏的指纹配准方法，该方法通过密集的指纹补丁对齐和匹配过程来估计一对指纹之间的空间变换。给定一对匹配的指纹，我们绕过细节提取步骤，将统一采样的点作为关键点。然后，所提出的补丁对齐和匹配算法比较所有成对的采样点，并产生它们的相似性以及对齐参数。最后，通过光谱聚类找到了一组一致的对应关系。在NIST27数据库和MOLF数据库上进行的大量实验表明，该方法可以实现最新的注册性能，尤其是在具有挑战性的条件下。代码在以下位置公开提供：在NIST27数据库和MOLF数据库上进行的大量实验表明，该方法可以实现最新的注册性能，尤其是在具有挑战性的条件下。代码在以下位置公开提供：在NIST27数据库和MOLF数据库上进行的大量实验表明，该方法可以实现最新的注册性能，尤其是在具有挑战性的条件下。代码在以下位置公开提供：https://github.com/Gus233/Latent-Fingerprint-Registration 。