Finger-vein pattern identification using principal component analysis and the neural network technique

Jian Da Wu, Chiung Tsiung Liu

Research output: Contribution to journalArticle

68 Citations (Scopus)

Abstract

This paper presents a personal identification system using finger-vein patterns with component analysis and neural network technology. In the proposed system, the finger-vein patterns are captured by a device that can transmit near infrared through the finger and record the patterns for signal analysis. The proposed biometric system for verification consists of a combination of feature extraction using principal component analysis (PCA) and pattern classification using back-propagation (BP) network and adaptive neuro-fuzzy inference system (ANFIS). Finger-vein features are first extracted by PCA method to reduce the computational burden and removes noise residing in the discarded dimensions. The features are then used in pattern classification and identification. To verify the effect of the proposed ANFIS in the pattern classification, the BP network is compared with the proposed system. The experimental results indicated the proposed system using ANFIS has better performance than the BP network for personal identification using the finger-vein patterns.

Original languageEnglish
Pages (from-to)5423-5427
Number of pages5
JournalExpert Systems with Applications
Volume38
Issue number5
DOIs
Publication statusPublished - 2011 May 1

Fingerprint

Fuzzy inference
Backpropagation
Principal component analysis
Pattern recognition
Neural networks
Adaptive systems
Signal analysis
Biometrics
Feature extraction
Identification (control systems)
Infrared radiation

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

Cite this

@article{a620be7bb4604e44a9d98bf96217841f,
title = "Finger-vein pattern identification using principal component analysis and the neural network technique",
abstract = "This paper presents a personal identification system using finger-vein patterns with component analysis and neural network technology. In the proposed system, the finger-vein patterns are captured by a device that can transmit near infrared through the finger and record the patterns for signal analysis. The proposed biometric system for verification consists of a combination of feature extraction using principal component analysis (PCA) and pattern classification using back-propagation (BP) network and adaptive neuro-fuzzy inference system (ANFIS). Finger-vein features are first extracted by PCA method to reduce the computational burden and removes noise residing in the discarded dimensions. The features are then used in pattern classification and identification. To verify the effect of the proposed ANFIS in the pattern classification, the BP network is compared with the proposed system. The experimental results indicated the proposed system using ANFIS has better performance than the BP network for personal identification using the finger-vein patterns.",
author = "Wu, {Jian Da} and Liu, {Chiung Tsiung}",
year = "2011",
month = "5",
day = "1",
doi = "10.1016/j.eswa.2010.10.013",
language = "English",
volume = "38",
pages = "5423--5427",
journal = "Expert Systems with Applications",
issn = "0957-4174",
publisher = "Elsevier Limited",
number = "5",

}

Finger-vein pattern identification using principal component analysis and the neural network technique. / Wu, Jian Da; Liu, Chiung Tsiung.

In: Expert Systems with Applications, Vol. 38, No. 5, 01.05.2011, p. 5423-5427.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Finger-vein pattern identification using principal component analysis and the neural network technique

AU - Wu, Jian Da

AU - Liu, Chiung Tsiung

PY - 2011/5/1

Y1 - 2011/5/1

N2 - This paper presents a personal identification system using finger-vein patterns with component analysis and neural network technology. In the proposed system, the finger-vein patterns are captured by a device that can transmit near infrared through the finger and record the patterns for signal analysis. The proposed biometric system for verification consists of a combination of feature extraction using principal component analysis (PCA) and pattern classification using back-propagation (BP) network and adaptive neuro-fuzzy inference system (ANFIS). Finger-vein features are first extracted by PCA method to reduce the computational burden and removes noise residing in the discarded dimensions. The features are then used in pattern classification and identification. To verify the effect of the proposed ANFIS in the pattern classification, the BP network is compared with the proposed system. The experimental results indicated the proposed system using ANFIS has better performance than the BP network for personal identification using the finger-vein patterns.

AB - This paper presents a personal identification system using finger-vein patterns with component analysis and neural network technology. In the proposed system, the finger-vein patterns are captured by a device that can transmit near infrared through the finger and record the patterns for signal analysis. The proposed biometric system for verification consists of a combination of feature extraction using principal component analysis (PCA) and pattern classification using back-propagation (BP) network and adaptive neuro-fuzzy inference system (ANFIS). Finger-vein features are first extracted by PCA method to reduce the computational burden and removes noise residing in the discarded dimensions. The features are then used in pattern classification and identification. To verify the effect of the proposed ANFIS in the pattern classification, the BP network is compared with the proposed system. The experimental results indicated the proposed system using ANFIS has better performance than the BP network for personal identification using the finger-vein patterns.

UR - http://www.scopus.com/inward/record.url?scp=79151470570&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79151470570&partnerID=8YFLogxK

U2 - 10.1016/j.eswa.2010.10.013

DO - 10.1016/j.eswa.2010.10.013

M3 - Article

AN - SCOPUS:79151470570

VL - 38

SP - 5423

EP - 5427

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

IS - 5

ER -