Evaluate and determine the most appropriate method to identify finger vein

Sepehr Damavandinejadmonfared*, Ali Khalili Mobarakeh, Shahrel Azmin Suandi, Bakhtiar Affendi Rosdi

*Corresponding author for this work

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

In this paper, the performance of a variety of different methods of dimensionality reduction on finger vein database is evaluated to determine the most appropriate one in terms of finger vein recognition. Principal Component Analysis using K-nearest neighbor (KNN) as a classifier, different types of Kernel Principal Component Analysis (KPCA) using KNN as a classifier, different types of Kernel Entropy Component Analysis (KECA) using KNN as a classifier, and finally different types of KPCA using Local mean-based k-nearest centroid neighbor (LMKNCN) as a classifier are implemented on finger vein database. Different types of KPCA and KECA used in this experiment are Linear, Polynomial, and Gaussian. Extensive comparisons are made in this paper to identify which method matches finger vein recognition best.

Original languageEnglish
Pages (from-to)516-521
Number of pages6
JournalProcedia Engineering
Volume41
DOIs
Publication statusPublished - 2012
Externally publishedYes

Keywords

  • Biometrics
  • Finger vein recognition
  • Kernel Entropy Component Analysis (KPCA).Nomenclature
  • Kernel Principal Component Anlysis (KPCA)
  • Principal Component Analysis (PCA)

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