A Single-step Accurate Fingerprint Registration Method Based on Local Feature Matching

TL;DR

This paper introduces a novel end-to-end single-step fingerprint registration method that directly predicts matching points, improving accuracy and robustness especially for low-quality images, and achieving state-of-the-art performance.

Contribution

The proposed method is a single-step, end-to-end approach that directly predicts semi-dense matching points, reducing failure rates associated with minutiae-based registration.

Findings

Achieves state-of-the-art fingerprint matching performance.

Effective for low-quality fingerprint images.

Can be combined with dense registration algorithms for enhanced accuracy.

Abstract

Distortion of the fingerprint images leads to a decline in fingerprint recognition performance, and fingerprint registration can mitigate this distortion issue by accurately aligning two fingerprint images. Currently, fingerprint registration methods often consist of two steps: an initial registration based on minutiae, and a dense registration based on matching points. However, when the quality of fingerprint image is low, the number of detected minutiae is reduced, leading to frequent failures in the initial registration, which ultimately causes the entire fingerprint registration process to fail. In this study, we propose an end-to-end single-step fingerprint registration algorithm that aligns two fingerprints by directly predicting the semi-dense matching points correspondences between two fingerprints. Thus, our method minimizes the risk of minutiae registration failure and also leverages global-local attentions to achieve end-to-end pixel-level alignment between the two fingerprints. Experiment results prove that our method can achieve the state-of-the-art matching performance with only single-step registration, and it can also be used in conjunction with dense registration algorithms for further performance improvements.