Finger vein recognition using straight line approximation based on ensemble learning

Research output: Contribution to journalArticle

Abstract

Human identity recognition and protection of information security are current global concerns in this age of increasing information growth. Biometrics approach of defining identity is considered as one of the highly potential approaches due to its internal feature that is difficult to be artificially recreated, stolen and/or forgotten. The new recognition system based on finger vein is a unique method depending on physiological traits and parameters of the vein patterns for the human. Published works on finger vein identification have hitherto ignored the power of aggregating different types of features and classifiers in improving the performance of the biometric recognition system. In this paper, we developed a novel feature approach named as straight line approximator (SLA) for extending the feature space of vein pattern using a public data set SDUMLA-HMT comprising about 3,816 images of finger vein for 160 persons. Furthermore, we applied a set of extreme learning machine (ELM) and support vector machine (SVM) classifier in different kernels. Then, we used the combination rules to improve the performance of the system. The experiment result of the proposed method achieved an accuracy of 87% using (DS and GWAR) rules at rank 1, while the accuracy of DS rule 93% and GWAR rule 92% at rank 5.

Original languageEnglish
Pages (from-to)153-159
Number of pages7
JournalInternational Journal of Advanced Computer Science and Applications
Volume10
Issue number1
DOIs
Publication statusPublished - 1 Jan 2019

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Biometrics
Classifiers
Security of data
Support vector machines
Learning systems
Experiments

Keywords

  • ELM
  • Finger vein recognition
  • HOG
  • SLA
  • Straight line approximate
  • SVM

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

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title = "Finger vein recognition using straight line approximation based on ensemble learning",
abstract = "Human identity recognition and protection of information security are current global concerns in this age of increasing information growth. Biometrics approach of defining identity is considered as one of the highly potential approaches due to its internal feature that is difficult to be artificially recreated, stolen and/or forgotten. The new recognition system based on finger vein is a unique method depending on physiological traits and parameters of the vein patterns for the human. Published works on finger vein identification have hitherto ignored the power of aggregating different types of features and classifiers in improving the performance of the biometric recognition system. In this paper, we developed a novel feature approach named as straight line approximator (SLA) for extending the feature space of vein pattern using a public data set SDUMLA-HMT comprising about 3,816 images of finger vein for 160 persons. Furthermore, we applied a set of extreme learning machine (ELM) and support vector machine (SVM) classifier in different kernels. Then, we used the combination rules to improve the performance of the system. The experiment result of the proposed method achieved an accuracy of 87{\%} using (DS and GWAR) rules at rank 1, while the accuracy of DS rule 93{\%} and GWAR rule 92{\%} at rank 5.",
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