Document Type

Article

Publication Date

2022

DOI

10.3934/mbe.2022032

Publication Title

Mathematical Biosciences and Engineering

Volume

19

Issue

1

Pages

707-737

Abstract

Singular point detection is a primary step in fingerprint recognition, especially for fingerprint alignment and classification. But in present there are still some problems and challenges such as more false-positive singular points or inaccurate reference point localization. This paper proposes an accurate core point localization method based on spatial domain features of fingerprint images from a completely different viewpoint to improve the fingerprint core point displacement problem of singular point detection. The method first defines new fingerprint features, called furcation and confluence, to represent specific ridge/valley distribution in a core point area, and uses them to extract the innermost Curve of ridges. The summit of this Curve is regarded as the localization result. Furthermore, an approach for removing false Furcation and Confluence based on their correlations is developed to enhance the method robustness. Experimental results show that the proposed method achieves satisfactory core localization accuracy in a large number of samples.

Comments

© 2022 the Authors, licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Original Publication Citation

Ye, X., Shen, Y., Zeng, M., Liu, Y., Chen, H., & Zhao, Z. (2022). Core point pixel-level localization by fingerprint features in spatial domain. Mathematical Biosciences and Engineering, 19(1), 707-737. https://doi.org/10.3934/mbe.2022032

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