An Application for Singular Point Location in Fingerprint Classification

2011 ◽  
pp. 262-276 ◽  
Author(s):  
Ali Ismail Awad ◽  
Kensuke Baba
Keyword(s):  
Singular Point ◽  
Point Location ◽  
Fingerprint Classification

scholarly journals A Customized Semantic Segmentation Network for the Fingerprint Singular Point Detection

2020 ◽  
Vol 10 (11) ◽  
pp. 3868
Author(s):  
Jiong Chen ◽  
Heng Zhao ◽  
Zhicheng Cao ◽  
Fei Guo ◽  
Liaojun Pang
Keyword(s):  
Singular Point ◽  
Semantic Segmentation ◽  
Singular Points ◽  
Detection Methods ◽  
Orientation Field ◽  
Global Features ◽  
Fingerprint Image ◽  
Detection Rates ◽  
Fingerprint Classification ◽  
Point Detection

As one of the most important and obvious global features for fingerprints, the singular point plays an essential role in fingerprint registration and fingerprint classification. To date, the singular point detection methods in the literature can be generally divided into two categories: methods based on traditional digital image processing and those on deep learning. Generally speaking, the former requires a high-precision fingerprint orientation field for singular point detection, while the latter just needs the original fingerprint image without preprocessing. Unfortunately, detection rates of these existing methods, either of the two categories above, are still unsatisfactory, especially for the low-quality fingerprint. Therefore, regarding singular point detection as a semantic segmentation of the small singular point area completely and directly, we propose a new customized convolutional neural network called SinNet for segmenting the accurate singular point area, followed by a simple and fast post-processing to locate the singular points quickly. The performance evaluation conducted on the publicly Singular Points Detection Competition 2010 (SPD2010) dataset confirms that the proposed method works best from the perspective of overall indexes. Especially, compared with the state-of-art algorithms, our proposal achieves an increase of 10% in the percentage of correctly detected fingerprints and more than 16% in the core detection rate.

Fingerprint Sub-Classification and Singular Point Detection

1998 ◽  
Vol 12 (04) ◽  
pp. 407-422 ◽  
Author(s):  
Gustavo Drets ◽  
Hans Liljenström
Keyword(s):  
Singular Point ◽  
Detection Method ◽  
Field Pattern ◽  
Classification Procedure ◽  
Fingerprint Classification ◽  
Novel Approach ◽  
Directional Field ◽  
Forensic Expert ◽  
The Stability ◽  
Point Detection

This paper presents a novel approach to fingerprint singular point detection. Singular points (cores and deltas) are used for fingerprint classification, sub-classification and registration. This method exploits the stability of the directional field pattern extracted from singular point regions at different resolution levels. The procedure is invariant to translations, scaling and small rotations. A fingerprint sub-classification procedure was built based on the proposed singular point detection method. Two kinds of tests were conducted on a subset consisting of 955 NIST-14 fingerprint images. First, automatic and forensic expert sub-classifications were compared. Second, the consistency of the proposed method was measured comparing automatic sub-classification for two different rolls of the same fingerprint.

Fingerprint Classification by Ridgeline and Singular Point Analysis

2008 ◽  
Author(s):  
Wei Liu ◽  
Yonghui Chen ◽  
Fang Wan
Keyword(s):  
Singular Point ◽  
Fingerprint Classification ◽  
Point Analysis

First order over determined system with singular point

2007 ◽  
Vol 18 (1) ◽  
pp. 65-80
Author(s):  
Adel Nasim Adib ◽  
Nusrat Rajabov
Keyword(s):  
Singular Point ◽  
First Order
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