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Title: Assessing the Impact of Corneal Refraction and Iris Tissue Non-planarity on Iris Recognition

Abstract

Assumptions regarding eye morphology are implicit in iris recognition algorithms. The cornea is assumed to have little to no effect on the view of the iris texture when the eye gaze is non-frontal, the iris is assumed flat, and the eye is assumed to be imaged via an orthographic projection. If these assumptions hold, affine transformations may be used to rectify non-frontally posed images to a frontal view and the rubber sheet model may be accurately used to normalize the iris annulus to a rectangular image. This work examines how iris recognition performance degrades when the first two assumptions are violated. Using a computer renderable eye model, a data set is created varying the presence of the cornea and a parameterized non-planarity of the iris shape across a large range of eye gaze angles. Matching scores are created using a commercial matcher. Thus, when comparing the relative impact of each assumption violation, it is observed that iris non-planarity presents a more significant problem than corneal refractive distortion with regard to iris recognition accuracy in non-frontal images.

Authors:
 [1];  [1];  [2];  [2]
  1. Univ. of Notre Dame, Notre Dame, IN (United States)
  2. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1502556
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
IEEE Transactions on Information Forensics and Security
Additional Journal Information:
Journal Volume: 14; Journal ID: ISSN 1556-6013
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; Iris recognition; Cornea; Shape; Biological system modeling; Transforms; Optical distortion; Imaging; non-frontal recognition; ray-tracing; graphics; optical simulation

Citation Formats

Thompson, Joseph, Flynn, Patrick, Boehnen, Christopher, and Santos-Villalobos, Hector. Assessing the Impact of Corneal Refraction and Iris Tissue Non-planarity on Iris Recognition. United States: N. p., 2018. Web. doi:10.1109/TIFS.2018.2869342.
Thompson, Joseph, Flynn, Patrick, Boehnen, Christopher, & Santos-Villalobos, Hector. Assessing the Impact of Corneal Refraction and Iris Tissue Non-planarity on Iris Recognition. United States. doi:10.1109/TIFS.2018.2869342.
Thompson, Joseph, Flynn, Patrick, Boehnen, Christopher, and Santos-Villalobos, Hector. Mon . "Assessing the Impact of Corneal Refraction and Iris Tissue Non-planarity on Iris Recognition". United States. doi:10.1109/TIFS.2018.2869342. https://www.osti.gov/servlets/purl/1502556.
@article{osti_1502556,
title = {Assessing the Impact of Corneal Refraction and Iris Tissue Non-planarity on Iris Recognition},
author = {Thompson, Joseph and Flynn, Patrick and Boehnen, Christopher and Santos-Villalobos, Hector},
abstractNote = {Assumptions regarding eye morphology are implicit in iris recognition algorithms. The cornea is assumed to have little to no effect on the view of the iris texture when the eye gaze is non-frontal, the iris is assumed flat, and the eye is assumed to be imaged via an orthographic projection. If these assumptions hold, affine transformations may be used to rectify non-frontally posed images to a frontal view and the rubber sheet model may be accurately used to normalize the iris annulus to a rectangular image. This work examines how iris recognition performance degrades when the first two assumptions are violated. Using a computer renderable eye model, a data set is created varying the presence of the cornea and a parameterized non-planarity of the iris shape across a large range of eye gaze angles. Matching scores are created using a commercial matcher. Thus, when comparing the relative impact of each assumption violation, it is observed that iris non-planarity presents a more significant problem than corneal refractive distortion with regard to iris recognition accuracy in non-frontal images.},
doi = {10.1109/TIFS.2018.2869342},
journal = {IEEE Transactions on Information Forensics and Security},
number = ,
volume = 14,
place = {United States},
year = {2018},
month = {9}
}

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