A palmprint recognition algorithm using principal component analysis of phase information

Satoshi Iitsuka, Kazuyuki Miyazawa, Takafumi Aoki

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

9 Citations (Scopus)

Abstract

This paper presents a palmprint recognition algorithm using Principal Component Analysis (PCA) of phase information in 2D (two-dimensional) Discrete Fourier Transforms (DFTs) of palmprint images. To achieve highly robust palmprint recognition, the proposed algorithm (i) limits the frequency bandwidth, and (ii) averages phase spectra using multiple palmprint images captured from the same hand at an enrollment stage. Through a set of experiments, we demonstrate that the proposed method can significantly reduce computational cost without sacrificing recognition performance compared with our previous work using Phase-Only Correlation (POC) - an image matching technique using the phase components in 2D DFTs of given images. Also, the resulting performance is much higher than those of conventional palmprint recognition algorithms which apply PCA to palmprint images, or phase spectra directly.

Original languageEnglish
Title of host publication2009 IEEE International Conference on Image Processing, ICIP 2009 - Proceedings
PublisherIEEE Computer Society
Pages1973-1976
Number of pages4
ISBN (Print)9781424456543
DOIs
Publication statusPublished - 2009 Jan 1
Event2009 IEEE International Conference on Image Processing, ICIP 2009 - Cairo, Egypt
Duration: 2009 Nov 72009 Nov 10

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Other

Other2009 IEEE International Conference on Image Processing, ICIP 2009
CountryEgypt
CityCairo
Period09/11/709/11/10

Keywords

  • Biometrics
  • Palmprint recognition
  • Phase information
  • Principal component analysis

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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