Result: Fingerprint matching based on extreme learning machine : Extreme Learning Machines Theory & Applications

Title:
Fingerprint matching based on extreme learning machine : Extreme Learning Machines Theory & Applications
Authors:
JUCHENG YANG, SHANJUAN XIE, SOOK YOON, DONGSUN PARK, ZHIJUN FANG, SHOUYUAN YANG
Source:
Neural computing & applications (Print). 22(3-4):435-445
Publisher Information:
London: Springer, 2013.
Publication Year:
2013
Physical Description:
print, 26 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Neurology, Neurologie, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Analyse mathématique, Mathematical analysis, Calcul des variations et contrôle optimal, Calculus of variations and optimal control, Probabilités et statistiques, Probability and statistics, Statistiques, Statistics, Analyse multivariable, Multivariate analysis, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Méthodes numériques en programmation mathématique, optimisation et calcul variationnel, Numerical methods in mathematical programming, optimization and calculus of variations, Optimisation et calcul variationnel numériques, Numerical methods in optimization and calculus of variations, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Apprentissage et systèmes adaptatifs, Learning and adaptive systems, Accord fréquence, Tuning, Sintonización frecuencia, Calcul neuronal, Neural computation, computación neuronal, Classificateur, Classifier, Clasificador, Classification, Clasificación, Complexité calcul, Computational complexity, Complejidad computación, Empreinte digitale, Fingerprint, Huella digital, Etude comparative, Comparative study, Estudio comparativo, Etude méthode, Method study, Estudio método, Extraction caractéristique, Feature extraction, Homme, Human, Hombre, Invariant, Invariante, Moment statistique, Statistical moment, Momento estadístico, Méthode moindre carré, Least squares method, Método cuadrado menor, Méthode optimisation, Optimization method, Método optimización, Méthode à pas, Step method, Método a paso, Optimisation, Optimization, Optimización, Reconnaissance, Recognition, Reconocimiento, Réseau neuronal, Neural network, Red neuronal, Résultat expérimental, Experimental result, Resultado experimental, Traitement temps réel, Real time processing, Tratamiento tiempo real, 49XX, 62E17, 62F07, 62H30, 65K10, 65Kxx, Apprentissage machine, Extreme learning machine, Fingerprint matching, Invariant moments, Regularized
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, China
School of Electronics and Information Engineering, Chonbuk National University, Jeonbuk, Korea, Republic of
Department of Multimedia Engineering. Mokpo National University, Jeonnam, Korea, Republic of
ISSN:
0941-0643
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=27659224
Rights:
Copyright 2014 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems

Mathematics
Accession Number:
edscal.27659224
Database:
PASCAL Archive

Further Information

Considering fingerprint matching as a classification problem, the extreme learning machine (ELM) is a powerful classifier for assigning inputs to their corresponding classes, which offers better generalization performance, much faster learning speed, and minimal human intervention, and is therefore able to overcome the disadvantages of other gradient-based, standard optimization-based, and least squares-based learning techniques, such as high computational complexity, difficult parameter tuning, and so on. This paper proposes a novel fingerprint recognition system by first applying the ELM and Regularized ELM (R-ELM) to fingerprint matching to overcome the demerits of traditional learning methods. The proposed method includes the following steps: effective preprocessing, extraction of invariant moment features, and PCA for feature selection. Finally, ELM and R-ELM are used for fingerprint matching. Experimental results show that the proposed methods have a higher matching accuracy and are less time-consuming; thus, they are suitable for real-time processing. Other comparative studies involving traditional methods also show that the proposed methods with ELM and R-ELM outperform the traditional ones.