A survey of fingerprint classification Part I: taxonomies on feature extraction methods and learning models

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Date
2015Author
Version
Acceso abierto / Sarbide irekia
Type
Artículo / Artikulua
Version
Versión aceptada / Onetsi den bertsioa
Impact
|
10.1016/j.knosys.2015.02.008
Abstract
This paper reviews the fingerprint classification literature looking at the problem from a double perspective. We first deal with feature extraction methods, including the different models considered for singular point detection and for orientation map extraction. Then, we focus on the different learning models considered to build the classifiers used to label new fingerprints. Taxonomies and cla ...
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This paper reviews the fingerprint classification literature looking at the problem from a double perspective. We first deal with feature extraction methods, including the different models considered for singular point detection and for orientation map extraction. Then, we focus on the different learning models considered to build the classifiers used to label new fingerprints. Taxonomies and classifications for the feature extraction, singular point detection, orientation extraction and learning methods are presented. A critical view of the existing literature have led us to present a discussion on the existing methods and their drawbacks such as difficulty in their reimplementation, lack of details or major differences in their evaluations procedures. On this account, an experimental analysis of the most relevant methods is carried out in the second part of this paper, and a new method based on their combination is presented. [--]
Subject
Fingerprint classification,
Feature extraction,
Classification,
Fingerprint recognition,
SMV,
Neural networks,
Ensembles,
Orientation map,
Singular points
Publisher
Elsevier
Published in
Knowledge-Based Systems 81 (2015) 76–97
Departament
Universidad Pública de Navarra. Departamento de Automática y Computación /
Nafarroako Unibertsitate Publikoa. Automatika eta Konputazioa Saila
Publisher version
Sponsorship
This work was supported by the Research Projects CAB(CDTI),
TIN2011-28488, and TIN2013-40765-P.D