Iris Recognition: On the Segmentation of Degraded Images Acquired in the Visible Wavelength
- 31 July 2010
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in Ieee Transactions On Pattern Analysis and Machine Intelligence
- Vol. 32 (8), 1502-1516
- https://doi.org/10.1109/TPAMI.2009.140
Abstract
Iris recognition imaging constraints are receiving increasing attention. There are several proposals to develop systems that operate in the visible wavelength and in less constrained environments. These imaging conditions engender acquired noisy artifacts that lead to severely degraded images, making iris segmentation a major issue. Having observed that existing iris segmentation methods tend to fail in these challenging conditions, we present a segmentation method that can handle degraded images acquired in less constrained conditions. We offer the following contributions: 1) to consider the sclera the most easily distinguishable part of the eye in degraded images, 2) to propose a new type of feature that measures the proportion of sclera in each direction and is fundamental in segmenting the iris, and 3) to run the entire procedure in deterministically linear time in respect to the size of the image, making the procedure suitable for real-time applications.Keywords
This publication has 36 references indexed in Scilit:
- Extended Depth of Field Iris Recognition with Correlation FiltersPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2008
- Iris localization via intensity gradient and recognition through bit planesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2007
- Using Artificial Neural Networks and Feature Saliency Techniques for Improved Iris Segmentation2007 International Joint Conference on Neural Networks, 2007
- A new segmentation approach for iris recognition based on hand-held capture devicePattern Recognition, 2007
- Human eye localization using the modified Hough transformOptik, 2006
- Iris Recognition at a DistanceLecture Notes in Computer Science, 2005
- Phenotypic versus Genotypic Approaches to Face RecognitionPublished by Springer Science and Business Media LLC ,1998
- First- and Second-Order Methods for Learning: Between Steepest Descent and Newton's MethodNeural Computation, 1992
- An algorithm for linear least squares problems with equality and nonnegativity constraintsMathematical Programming, 1981
- Function minimization by conjugate gradientsThe Computer Journal, 1964