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Published in:
Introduction to the Handbook of Iris Recognition Read chapter Read first chapter

2016 | OriginalPaper | Chapter

8. Iris Recognition with Taylor Expansion Features

Authors : Algirdas Bastys, Justas Kranauskas, Volker Krüger

Published in: Handbook of Iris Recognition

Publisher: Springer London

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Abstract

The random distribution of features in an iris image texture allows to perform iris-based personal authentication with high confidence. In this chapter we describe three iris representations. The first one is a phase-based iris texture representation which is based on a binarized multi-scale Taylor expansion. The second one describes the iris by using the most significant local extremum points of the first two Taylor expansion coefficients. The third method is a combination of the first two representations. For all methods we provide efficient similarity measures which are robust to moderate iris segmentation inaccuracies. Using three public iris datasets, we show (a) the compact template size of the first two representations and (b) their effectiveness: the first two representations alone perform well already, but in combination, they outperform state-of-the-art iris recognition approaches significantly.

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previous chapter Methods for Iris Segmentation
next chapter Application of Correlation Filters for Iris Recognition
Footnotes
1
Shift along the x direction inherits angular periodicity.
 
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Metadata
Title
Iris Recognition with Taylor Expansion Features
Authors
Algirdas Bastys
Justas Kranauskas
Volker Krüger
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_8

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