Gender classification from iris images using fusion of uniform local binary patterns

Juan E. Tapia, Claudio A. Perez, Kevin W. Bowyer

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

33 Citations (Scopus)

Abstract

This paper is concerned in analyzing iris texture in order to determine “soft biometric”, attributes of a person, rather than identity. In particular, this paper is concerned with predicting the gender of a person based on analysis of features of the iris texture. Previous researchers have explored various approaches for predicting the gender of a person based on iris texture. We explore using different implementations of Local Binary Patterns from the iris image using the masked information. Uniform LBP with concatenated histograms significantly improves accuracy of gender prediction relative to using the whole iris image. Using a subject-disjoint test set, we are able to achieve over 91% correct gender prediction using the texture of the iris. To our knowledge, this is the highest accuracy yet achieved for predicting gender from iris texture.

Original languageEnglish
Title of host publicationComputer Vision - ECCV 2014 Workshops, Proceedings
EditorsCarsten Rother, Michael M. Bronstein, Lourdes Agapito
PublisherSpringer Verlag
Pages751-763
Number of pages13
ISBN (Electronic)9783319161808
DOIs
Publication statusPublished - 2015
Event13th European Conference on Computer Vision, ECCV 2014 - Zurich, Switzerland
Duration: 6 Sept 201412 Sept 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8926
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other13th European Conference on Computer Vision, ECCV 2014
Country/TerritorySwitzerland
CityZurich
Period6/09/1412/09/14

Keywords

  • Biometrics
  • Gender classification
  • Iris
  • LBP

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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