Wavelet packet correlation methods in biometrics

Pablo Hennings, Jason Thornton, Jelena Kovačević, B. V.K.Vijaya Kumar

Research output: Contribution to journalArticle

Abstract

We introduce wavelet packet correlation filter classifiers. Correlation filters are traditionally designed in the image domain by minimization of some criterion function of the image training set. Instead, we perform classification in wavelet spaces that have training set representations that provide better solutions to the optimization problem in the filter design. We propose a pruning algorithm to find these wavelet spaces by using a correlation energy cost function, and we describe a match score fusion algorithm for applying the filters trained across the packet tree. The proposed classification algorithm is suitable for any object-recognition task. We present results by implementing a biometric recognition system that uses the NIST 24 fingerprint database, and show that applying correlation filters in the wavelet domain results in considerable improvement of the standard correlation filter algorithm

Original languageEnglish (US)
Pages (from-to)637-646
Number of pages10
JournalApplied Optics
Volume44
Issue number5
DOIs
StatePublished - Feb 10 2005

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Engineering (miscellaneous)
  • Electrical and Electronic Engineering

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  • Cite this

    Hennings, P., Thornton, J., Kovačević, J., & Kumar, B. V. K. V. (2005). Wavelet packet correlation methods in biometrics. Applied Optics, 44(5), 637-646. https://doi.org/10.1364/AO.44.000637