Skin distortion is a long standing challenge in fingerprint matching, which causes false non-matches. Previous studies have shown that the recognition rate can be improved by estimating the distortion field from a distorted fingerprint and then rectifying it into a normal fingerprint. However, existing rectification methods are based on principal component representation of distortion fields, which is not accurate and are very sensitive to finger pose. In this paper, we propose a rectification method where a self-reference based network is utilized to directly estimate the dense distortion field of distorted fingerprint instead of its low dimensional representation. This method can output accurate distortion fields of distorted fingerprints with various finger poses. Considering the limited number and variety of distorted fingerprints in the existing public dataset, we collected more distorted fingerprints with diverse finger poses and distortion patterns as a new database. Experimental results demonstrate that our proposed method achieves the state-of-the-art rectification performance in terms of distortion field estimation and rectified fingerprint matching.

翻译：皮肤畸变是指纹匹配中一个长期存在的挑战，会导致错误的非匹配。以往研究表明，通过从畸变指纹中估计畸变场并将其校正为正常指纹，可以提升识别率。然而，现有校正方法基于畸变场的主成分表示，这种表示不够精确且对手指姿态非常敏感。本文提出一种校正方法，利用基于自参考的网络直接估计畸变指纹的密集畸变场，而非其低维表示。该方法能够输出具有不同手指姿态的畸变指纹的精确畸变场。鉴于现有公共数据集中畸变指纹的数量和多样性有限，我们收集了更多具有不同手指姿态和畸变模式的畸变指纹，构建了一个新数据库。实验结果表明，本方法在畸变场估计和校正指纹匹配方面达到了最先进的校正性能。