Coarse iris classification using box-counting to estimate fractal dimensions

Li Yu, Dapeng Zhang, Kuanquan Wang, Wen Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

72 Citations (Scopus)

Abstract

This paper proposes a novel algorithm for the automatic coarse classification of iris images using a box-counting method to estimate the fractal dimensions of the iris. First, the iris image is segmented into sixteen blocks, eight belonging to an upper group and eight to a lower group. We then calculate the fractal dimension value of these image blocks and take the mean value of the fractal dimension as the upper and the lower group fractal dimensions. Finally, all the iris images are classified into four categories in accordance with the upper and the lower group fractal dimensions. This classification method has been tested and evaluated on 872 iris cases, and the proportions of these categories in our database are 5.50%, 38.54%, 21.79%, and 34.17%. The iris images are classified with two algorithms, the double threshold algorithm, which classifies iris images with an accuracy of 94.61%, and the backpropagation algorithm, which is 93.23% accurate. When we allow for the border effect, the double threshold algorithm is 98.28% accurate.
Original languageEnglish
Pages (from-to)1791-1798
Number of pages8
JournalPattern Recognition
Volume38
Issue number11
DOIs
Publication statusPublished - 1 Nov 2005

Keywords

  • Box counting
  • Coarse classification
  • Fractal dimension
  • Iris image

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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