Pattern discrimination of joint transform correlator based on wavelet subband filtering

Li Chien Lin, Chau Jern Cheng*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


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
Issue number4-6
Publication statusPublished - 2004 Apr 1
Externally publishedYes

ASJC Scopus subject areas

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


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