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Entropy-Based Clustering Algorithm for Fingerprint Singular Point Detection

1
Institute of Photonic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
2
Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
3
Department of Computer Science and Information Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(8), 786; https://doi.org/10.3390/e21080786
Received: 19 July 2019 / Revised: 6 August 2019 / Accepted: 9 August 2019 / Published: 12 August 2019
(This article belongs to the Special Issue Statistical Machine Learning for Human Behaviour Analysis)
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Abstract

Fingerprints have long been used in automated fingerprint identification or verification systems. Singular points (SPs), namely the core and delta point, are the basic features widely used for fingerprint registration, orientation field estimation, and fingerprint classification. In this study, we propose an adaptive method to detect SPs in a fingerprint image. The algorithm consists of three stages. First, an innovative enhancement method based on singular value decomposition is applied to remove the background of the fingerprint image. Second, a blurring detection and boundary segmentation algorithm based on the innovative image enhancement is proposed to detect the region of impression. Finally, an adaptive method based on wavelet extrema and the Henry system for core point detection is proposed. Experiments conducted using the FVC2002 DB1 and DB2 databases prove that our method can detect SPs reliably. View Full-Text
Keywords: singular point detection; boundary segmentation; blurring detection; fingerprint image enhancement; fingerprint quality singular point detection; boundary segmentation; blurring detection; fingerprint image enhancement; fingerprint quality
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Le, N.T.; Le, D.H.; Wang, J.-W.; Wang, C.-C. Entropy-Based Clustering Algorithm for Fingerprint Singular Point Detection. Entropy 2019, 21, 786.

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