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Palmprint recognition via locality preserving projections and extreme learning machine neural network

  • Jiwen Lu*
  • , Yongwei Zhao
  • , Yanxue Xue
  • , Junlin Hu
  • *Corresponding author for this work
  • Xi'an University of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper proposes an efficient palmprint recognition method using Locality Preserving Projections (LPP) and extreme learning machine (ELM) neural network. Firstly, two-dimensional discrete wavelet transformation (DWT) is applied in the region of interest (ROI) of each palmprint image and then principal component analysis (PCA) and LPP are used for dimensionality reduction. Finally, we construct a single-hidden layer forward network (SLFN) to construct one extreme learning machine (ELM) to quickly classify the palmprint images. Experiments on the PolyU palmprint database demonstrate the effectiveness of the proposed method.

Original languageEnglish
Title of host publication2008 9th International Conference on Signal Processing, ICSP 2008
Pages2096-2099
Number of pages4
DOIs
StatePublished - 2008
Externally publishedYes
Event2008 9th International Conference on Signal Processing, ICSP 2008 - Beijing, China
Duration: 26 Oct 200829 Oct 2008

Publication series

NameInternational Conference on Signal Processing Proceedings, ICSP

Conference

Conference2008 9th International Conference on Signal Processing, ICSP 2008
Country/TerritoryChina
CityBeijing
Period26/10/0829/10/08

Keywords

  • Extreme learning machine (ELM)
  • Locality preserving projections (LPP)
  • Palmprint recognition

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