Pattern discrimination of joint transform correlator based on wavelet subband filtering

Li Chien Lin, Chau-Jern Cheng

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

We propose and demonstrate a Gabor wavelet prefiltering prior to classical and binarized joint transform correlator implementation to enhance texture features of fingerprints. The frequency- and orientation-selective properties of the wavelet subband filter are utilized to extract important textural features for optimal correlation recognition. A selection criterion for wavelet subbands is derived, and it is shown that the maximum signal-to-noise ratio of the correlator is achieved by optimizing the threshold level. Simulation results show that the proposed method increases the discrimination power of the correlator, especially under noisy environments.

Original languageEnglish
Pages (from-to)283-296
Number of pages14
JournalOptics Communications
Volume233
Issue number4-6
DOIs
Publication statusPublished - 2004 Apr 1

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Correlators
correlators
discrimination
Signal to noise ratio
signal to noise ratios
textures
Textures
filters
thresholds
simulation

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Physical and Theoretical Chemistry
  • Electrical and Electronic Engineering

Cite this

Pattern discrimination of joint transform correlator based on wavelet subband filtering. / Lin, Li Chien; Cheng, Chau-Jern.

In: Optics Communications, Vol. 233, No. 4-6, 01.04.2004, p. 283-296.

Research output: Contribution to journalArticle

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