Finger-vein pattern identification using SVM and neural network technique

Jian-Da Wu, Chiung Tsiung Liu

Research output: Contribution to journalArticle

58 Citations (Scopus)

Abstract

This paper presents a support vector machine (SVM) technique for finger-vein pattern identification in a personal identification system. Finger-vein pattern identification is one of the most secure and convenient techniques for personal identification. In the proposed system, the finger-vein pattern is captured by infrared LED and a CCD camera because the vein pattern is not easily observed in visible light. The proposed verification system consists of image pre-processing and pattern classification. In the work, principal component analysis (PCA) and linear discriminant analysis (LDA) are applied to the image pre-processing as dimension reduction and feature extraction. For pattern classification, this system used an SVM and adaptive neuro-fuzzy inference system (ANFIS). The PCA method is used to remove noise residing in the discarded dimensions and retain the main feature by LDA. The features are then used in pattern classification and identification. The accuracy of classification using SVM is 98% and only takes 0.015 s. The result shows a superior performance to the artificial neural network of ANFIS in the proposed system.

Original languageEnglish
Pages (from-to)14284-14289
Number of pages6
JournalExpert Systems with Applications
Volume38
Issue number11
DOIs
Publication statusPublished - 2011 Oct 1

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Pattern recognition
Support vector machines
Identification (control systems)
Fuzzy inference
Discriminant analysis
Neural networks
Principal component analysis
CCD cameras
Processing
Light emitting diodes
Feature extraction
Infrared radiation

All Science Journal Classification (ASJC) codes

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

Cite this

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abstract = "This paper presents a support vector machine (SVM) technique for finger-vein pattern identification in a personal identification system. Finger-vein pattern identification is one of the most secure and convenient techniques for personal identification. In the proposed system, the finger-vein pattern is captured by infrared LED and a CCD camera because the vein pattern is not easily observed in visible light. The proposed verification system consists of image pre-processing and pattern classification. In the work, principal component analysis (PCA) and linear discriminant analysis (LDA) are applied to the image pre-processing as dimension reduction and feature extraction. For pattern classification, this system used an SVM and adaptive neuro-fuzzy inference system (ANFIS). The PCA method is used to remove noise residing in the discarded dimensions and retain the main feature by LDA. The features are then used in pattern classification and identification. The accuracy of classification using SVM is 98{\%} and only takes 0.015 s. The result shows a superior performance to the artificial neural network of ANFIS in the proposed system.",
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Finger-vein pattern identification using SVM and neural network technique. / Wu, Jian-Da; Liu, Chiung Tsiung.

In: Expert Systems with Applications, Vol. 38, No. 11, 01.10.2011, p. 14284-14289.

Research output: Contribution to journalArticle

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