Iris Recognition with Image Segmentation Employing Retrained Off-the-Shelf Deep Neural Networks

Daniel Kerrigan, Mateusz Trokielewicz, A. Czajka, K. Bowyer
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引用次数: 23

Abstract

This paper offers three new, open-source, deep learning-based iris segmentation methods, and the methodology how to use irregular segmentation masks in a conventional Gabor-wavelet-based iris recognition. To train and validate the methods, we used a wide spectrum of iris images acquired by different teams and different sensors and offered publicly, including data taken from CASIA-Iris-Interval-v4, BioSec, ND-Iris-0405, UBIRIS, Warsaw-BioBase-Post-Mortem-Iris v2.0 (post-mortem iris images), and ND-TWINS-2009-2010 (iris images acquired from identical twins). This varied training data should increase the generalization capabilities of the proposed segmentation techniques. In database-disjoint training and testing, we show that deep learning-based segmentation outperforms the conventional (OSIRIS) segmentation in terms of Intersection over Union calculated between the obtained results and manually annotated ground-truth. Interestingly, the Gabor-based iris matching is not always better when deep learning-based segmentation is used, and is on par with the method employing Daugman’s based segmentation.
虹膜识别与图像分割采用再训练现成的深度神经网络
本文提出了三种新的、开源的、基于深度学习的虹膜分割方法,以及如何在传统的基于gabor小波的虹膜识别中使用不规则分割掩模的方法。为了训练和验证方法,我们使用了不同团队和不同传感器获得的广泛的虹膜图像,包括CASIA-Iris-Interval-v4、BioSec、ND-Iris-0405、UBIRIS、war - bibase - post-mortem - iris v2.0(死后虹膜图像)和ND-TWINS-2009-2010(从同卵双胞胎获得的虹膜图像)的数据。这种不同的训练数据应该增加所提出的分割技术的泛化能力。在数据库不相交的训练和测试中,我们发现基于深度学习的分割在获得的结果和人工标注的ground-truth之间计算的交集与联合方面优于传统的(OSIRIS)分割。有趣的是,当使用基于深度学习的分割时,基于gabor的虹膜匹配并不总是更好,并且与使用基于Daugman的分割的方法相当。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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