Fusion of multiple texture representations for palmprint recognition using neural networks

Galal M. Binmakhashen*, El Sayed M. El-Alfy

*Corresponding author for this work

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

2 Scopus citations

Abstract

During the last decade, palmprint recognition has received an increasing attention due to the abundant features that can be extracted from the captured palmprint image. However, a single palmprint texture representation may not be sufficient for reliable recognition. Therefore, in this paper we propose a computational model for palmprint recognition/identification by fusing different categories of feature-level representations using a multilayer perceptron (MLP) neural network. Features are extracted using Gabor filters and Principle Component Analysis (PCA) is used to reduce the dimensionality of the feature space by selecting the most relevant features for recognition. The proposed model has shown promising results in comparison with naïve Bayes, rule based and K*algorithm.

Original languageEnglish
Title of host publicationNeural Information Processing - 19th International Conference, ICONIP 2012, Proceedings
Pages410-417
Number of pages8
EditionPART 5
DOIs
StatePublished - 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 5
Volume7667 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Biometrics
  • Gabor Filter
  • Machine Learning
  • Multilayer Perceptron
  • Palmprint
  • Pattern Recognition
  • Principle Component Analysis

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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