Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/67515
Title: An accurate iris segmentation framework under relaxed imaging constraints using total variation mode
Authors: Zhao, Z
Pathak, AK 
Keywords: Iris recognition
Image segmentation
Lighting
Imaging
Noise measurement
Robustness
Databases
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers
Source: 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 7-13 Dec 2015, p.3828-3836 How to cite?
Abstract: This paper proposes a novel and more accurate iris segmentation framework to automatically segment iris region from the face images acquired with relaxed imaging under visible or near-infrared illumination, which provides strong feasibility for applications in surveillance, forensics and the search for missing children, etc. The proposed framework is built on a novel total-variation based formulation which uses l1 norm regularization to robustly suppress noisy texture pixels for the accurate iris localization. A series of novel and robust post processing operations are introduced to more accurately localize the limbic boundaries. Our experimental results on three publicly available databases, i.e., FRGC, UBIRIS.v2 and CASIA.v4-distance, achieve significant performance improvement in terms of iris segmentation accuracy over the state-of-the-art approaches in the literature. Besides, we have shown that using iris masks generated from the proposed approach helps to improve iris recognition performance as well. Unlike prior work, all the implementations in this paper are made publicly available to further advance research and applications in biometrics at-d-distance.
URI: http://hdl.handle.net/10397/67515
ISBN: 978-1-4673-8391-2 (electronic)
978-1-4673-8390-5 (USB)
EISSN: 2380-7504
DOI: 10.1109/ICCV.2015.436
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