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.
翻译:本文介绍了三种新的开放源码、基于深层次学习的虹膜分解方法,以及如何在传统的加博-波浪-电磁识别法中使用非正常分解面罩的方法。为了培训和验证这些方法,我们使用了由不同团队和不同传感器获得并公开提供的范围广泛的虹膜图像,包括从CASIA-Iris-Interval-v4、Biosec、ND-Iris-0405、UBIRIS、华沙-BioBase-Post-Mortem-Iris v2.0(死后iris图像)和ND-TWINS-2009-2010(从同双胞胎获得的图像)中获取的数据。这种不同的培训数据应当提高拟议分解技术的通用能力。在数据库脱节培训和测试中,我们表明深层次的分解法比在所获得的结果和手动附加说明的地面图中计算出的常规(OSIRIS)分解分解法的分解。有趣的是,在使用深层分解法的基础上使用Dagman的分解法。