Direct Regression of Distortion Field from a Single Fingerprint Image

TL;DR

This paper introduces a novel deep learning approach for directly estimating dense distortion fields from a single fingerprint image, significantly improving fingerprint rectification accuracy across diverse poses.

Contribution

It proposes a self-reference based neural network for direct distortion field estimation, surpassing traditional PCA-based methods in accuracy and pose robustness.

Findings

Achieves state-of-the-art distortion field estimation

Improves fingerprint matching accuracy after rectification

Introduces a new diverse fingerprint distortion dataset

Abstract

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.