Deducting Fingerprint Singular Points Using Orientation Field Reliability

2011 ◽  
Author(s):  
Mohammed S. Khalil
Keyword(s):  
Singular Points ◽  
Orientation Field ◽  
Field Reliability

Fingerprint Singular Point Detection Using Orientation Field Reliability

2011 ◽  
Vol 403-408 ◽  
pp. 4499-4506 ◽  
Author(s):  
Ravinder Kumar ◽  
Pravin Chandra ◽  
M. Hanmandlu
Keyword(s):  
Singular Point ◽  
Detection Rate ◽  
Singular Points ◽  
Identification System ◽  
Fingerprint Matching ◽  
Orientation Field ◽  
Point Detection ◽  
Field Reliability ◽  
Fingerprint Alignment ◽  
Novel Algorithm

Singular point detection is the most important step in Automatic Fingerprint Identification System (AFIS) and is used in fingerprint alignment, fingerprint matching, and particularly in classification. The computation of orientation field of a fingerprint can be verified by computing orientation field reliability. The most unreliable portion in orientation field can be the possible location of singular points. In this paper we have proposed a novel algorithm for detecting singular points using reliability of the fingerprint orientation field. Experimental results show that the proposed algorithm accurately detects singular points (core and delta) with the detection rate of 92.6 %.

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 classification based on decision tree from singular points and orientation field

2014 ◽  
Vol 41 (2) ◽  
pp. 752-764 ◽  
Author(s):  
Jing-Ming Guo ◽  
Yun-Fu Liu ◽  
Jla-Yu Chang ◽  
Jiann-Der Lee
Keyword(s):  
Decision Tree ◽  
Singular Points ◽  
Orientation Field ◽  
Fingerprint Classification

Binary diagrams of mesomorphous compounds exhibiting singular points

1978 ◽  
Vol 3 ◽  
pp. 381-386 ◽  
Author(s):  
F. Hardouin ◽  
G. Sigaud ◽  
M.-F. Achard ◽  
H. Gasparoux
Keyword(s):  
Singular Points

Effect of proximity of the Fermi level to singular points in the band structure on the kinetic and lattice properties of metals and alloys

1988 ◽  
Vol 154 (3) ◽  
pp. 525 ◽  
Author(s):  
V.P. Antropov ◽  
Valentin G. Vaks ◽  
M.I. Katsnel'son ◽  
V.G. Koreshkov ◽  
A.I. Likhtenshtein ◽  
...  
Keyword(s):  
Band Structure ◽  
Fermi Level ◽  
Metals And Alloys ◽  
Singular Points
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