Finger vein recognition using linear kernel entropy component analysis

Sepehr Damavandinejadmonfared*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

6 Citations (Scopus)

Abstract

Based on the previous research, Kernel Entropy Component Analysis (KECA) is introduced as a more appropriate method than Kernel Principal Component Analysis (KPCA) for face recognition. In this paper, an algorithm using KECA is proposed to merit finger vein recognition. The proposed algorithm is then compared to Principal Component Analysis (PCA) and Different types of KECA in order to determine the most appropriate one in terms of finger vein recognition.

Original languageEnglish
Title of host publicationProceedings - 2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012
Pages249-252
Number of pages4
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012 - Cluj-Napoca
Duration: 30 Aug 20121 Sep 2012

Other

Other2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing, ICCP 2012
CityCluj-Napoca
Period30/08/121/09/12

Keywords

  • Biometrics
  • finger vein recognition
  • Kernel Entropy Component Analysis (KPCA)
  • Principal Component Analysis (PCA)

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