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

Jian Da Wu, Chiung Tsiung Liu

Research output: Contribution to journalArticle

73 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

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All Science Journal Classification (ASJC) codes

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

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