@techreport{TR-IC-06-12,
  number = {IC-06-12},
  author = {Javier A. Montoya Zegarra and Neucimar J. Leite and
            Ricardo da S. Torres},
  title = {Wavelet-based Feature Extraction for Fingerprint Image Retrieval},
  month = {September},
  year = {2006}, 
  institution = {Institute of Computing, University of Campinas},
  note = {In English, 23 pages.
    \par\selectlanguage{english}\textbf{Abstract}

       This paper presents a novel approach to fingerprint retrieval
       for personal identification by joining three image retrieval tasks,
       namely, feature extraction, similarity measurement, and feature
       indexing, into a wavelet-based fingerprint retrieval system.
 
       \par

       We propose the use of different types of Wavelets for
       representing and describing the textural information present in
       fingerprint images. For that purposes, the feature vectors used
       to characterize the fingerprints are obtained by computing the
       mean and the standard deviation of the decomposed images in the
       Wavelet domain. These feature vectors are used to retrieve the
       most similar fingerprints given a query image, while their
       indexation is used to reduce the search spaces of image
       candidates. The different types of Wavelets used in our study
       include: Gabor Wavelets (GWs), Tree-Structured Wavelet
       Decomposition using both Orthogonal Filter Banks (TOWT) and
       Bi-orthogonal Filter Banks (TBOWT), as well as the Steerable
       Wavelets.

       \par

       To evaluate the retrieval accuracy of the proposed approach, a
       total number of eight different data sets were
       used. Experiments also evaluated different combinations of
       Wavelets with six similarity measures. The results show that
       the Gabor Wavelets combined with the Square Chord similarity
       measure achieves the best retrieval effectiveness.
  }
}