Robust reference point detection using gradient of fingerprint direction and feature extraction method

Junbum Park, Hanseok Ko

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

A novel reference point detection method is proposed by exploiting the GPM(Gradient Probabilistic Model) that captures the curvature information of fingerprint texture. The detection of reference point is accomplished through searching and locating the points of occurrence of the most evenly distributed gradient in probabilistic sense. We also propose a novel filterbank method to improve shortcoming of existing filterbank method in verification part. Existing filterbank method can lose the discerning attributes because the sectors of the outer band from the reference point are larger in size than those of the inner bands. Such shortcomings of the filterbank method are resolved by maintaining the attribute regions to equal size.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsPeter M. A. Sloot, David Abramson, Alexander V. Bogdanov, Yuriy E. Gorbachev, Jack J. Dongarra, Albert Y. Zomaya
PublisherSpringer Verlag
Pages1089-1099
Number of pages11
ISBN (Print)3540401970, 9783540401971
DOIs
Publication statusPublished - 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2660
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Park, J., & Ko, H. (2003). Robust reference point detection using gradient of fingerprint direction and feature extraction method. In P. M. A. Sloot, D. Abramson, A. V. Bogdanov, Y. E. Gorbachev, J. J. Dongarra, & A. Y. Zomaya (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 1089-1099). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2660). Springer Verlag. https://doi.org/10.1007/3-540-44864-0_113