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Introduction to the Handbook of Iris Recognition Read chapter Read first chapter

2016 | OriginalPaper | Chapter

14. Periocular Recognition from Low-Quality Iris Images

Authors : Josh Klontz, Mark J. Burge

Published in: Handbook of Iris Recognition

Publisher: Springer London

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Abstract

Definitions of the periocular region vary, but typically encompass the skin covering the orbit of the eye. Especially in cases where the iris has not been acquired with sufficient quality to reliably compute an IrisCode, the periocular region can provide additional discriminative information for biometric identification. The NIR periocular images which form NIST’s Face and Ocular Challenge Series (FOCS) are characterized by large variations in illumination, eyelid and eyelash occlusion, de-focus blur, motion blur, and low resolution. We investigate periocular recognition on the FOCS dataset using three distinct classes of features: photometric, keypoint, and frequency-based. We examine the performance of these features alone, in combination, and when fused with classic IrisCodes.

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previous chapter Iris Segmentation for Challenging Periocular Images
next chapter Unconstrained Iris Recognition in Visible Wavelengths
Footnotes
1
As can be expected for one of the earlier standard test sets like CASIA [17], the commercial algorithm was able to perfectly separate match and non-match pairs.
 
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Metadata
Title
Periocular Recognition from Low-Quality Iris Images
Authors
Josh Klontz
Mark J. Burge
Copyright Year
2016
Publisher
Springer London
Book
Handbook of Iris Recognition

Print ISBN: 978-1-4471-6782-2

Electronic ISBN: 978-1-4471-6784-6

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-1-4471-6784-6_14

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