Wavelet based independent component analysis for palmprint identification

Guang Ming Lu, Kuan Quan Wang, Dapeng Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

32 Citations (Scopus)

Abstract

This paper presents a multi-resolution analysis based Independent Component Analysis (ICA) method for automatic palmprint identification. The ICA is well known by its feature representation ability recently, in which the desired representation is the one that minimizes the statistical independence of the components of the representation. Such a representation can capture the essential feature and the structure of the palmprint images. At the same time, the palmprints have a great deal of different features, such as principal lines, wrinkles, ridges, minutiae points and texture, which can be regarded as multi-scale features. Then, it is reasonable for us to integrate the multi-resolution analysis method and ICA to represent the palmprint features. The experiment results show that the integrated method is more efficient than ICA algorithm.
Original languageEnglish
Title of host publicationProceedings of 2004 International Conference on Machine Learning and Cybernetics
Pages3547-3550
Number of pages4
Volume6
Publication statusPublished - 2 Nov 2004
EventProceedings of 2004 International Conference on Machine Learning and Cybernetics - Shanghai, China
Duration: 26 Aug 200429 Aug 2004

Conference

ConferenceProceedings of 2004 International Conference on Machine Learning and Cybernetics
CountryChina
CityShanghai
Period26/08/0429/08/04

Keywords

  • Independent Component Analysis
  • Multi-resolution Analysis
  • Palmprint Identification

ASJC Scopus subject areas

  • Engineering(all)

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