Off-Angle Iris Correction Methods

David S. Bolme, Hector Santos-Villalobos, Joseph Thompson, Mahmut Karakaya, Chris Bensing Boehnen

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

3 Scopus citations

Abstract

In many real-world iris recognition systems, obtaining consistent frontal images is problematic do to inexperienced or uncooperative users, untrained operators, or distracting environments. As a result many collected images are unusable by modern iris matchers. In this chapter, we present four methods for correcting off-angle iris images to appear frontal which makes them compatible with existing iris matchers. The methods include an affine correction, a retraced model of the human eye, measured displacements, and a genetic algorithm optimized correction. The affine correction represents a simple way to create an iris image that appears frontal but it does not account for refractive distortions of the cornea. The other method account for refraction. The retraced model simulates the optical properties of the cornea. The other two methods are data-driven. The first uses optical flow to measure the displacements of the iris texture when compared to frontal images of the same subject. The second uses a genetic algorithm to learn a mapping that optimizes the Hamming Distance scores between off-angle and frontal images. In this paper, we hypothesize that the biological model presented in our earlier work does not adequately account for all variations in eye anatomy and therefore the two data-driven approaches should yield better performance. Results are presented using the commercial VeriEye matcher that show that the genetic algorithm method clearly improves over prior work and makes iris recognition possible up to 50 off-angle.

Original languageEnglish
Title of host publicationAdvances in Computer Vision and Pattern Recognition
PublisherSpringer Science and Business Media Deutschland GmbH
Pages497-518
Number of pages22
DOIs
StatePublished - 2016

Publication series

NameAdvances in Computer Vision and Pattern Recognition
ISSN (Print)2191-6586
ISSN (Electronic)2191-6594

Keywords

  • Affine Transformation
  • Iris Boundary
  • Iris Image
  • Iris Recognition
  • Optical Flow

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