Previous work on iris recognition focused on either visible light (VL), near-infrared (NIR) imaging, or their fusion. However, limited numbers of works have investigated cross-spectral matching or compared the iris biometric performance under both VL and NIR spectrum using unregistered iris images taken from the same subject. To the best of our knowledge, this is the first work that proposes a framework for cross-spectral iris matching using unregistered iris images. To this end, three descriptors are proposed namely, Gabor-difference of Gaussian (G-DoG), Gabor-binarized statistical image feature (G-BSIF), and Gabor-multi-scale Weberface (G-MSW) to achieve robust cross-spectral iris matching. In addition, we explore the differences in iris recognition performance across the VL and NIR spectra. The experiments are carried out on the UTIRIS database which contains iris images acquired with both VL and NIR spectra for the same subject. Experimental and comparison results demonstrate that the proposed framework achieves state-of-the-art cross-spectral matching. In addition, the results indicate that the VL and NIR images provide complementary features for the iris pattern and their fusion improves notably the recognition performance.

中文翻译：

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跨谱虹膜匹配的新框架

虹膜识别的先前工作主要集中在可见光（VL），近红外（NIR）成像或它们的融合上。然而，有限数量的作品已经研究了跨谱匹配或使用来自同一对象的未注册虹膜图像比较了VL和NIR光谱下的虹膜生物测定性能。据我们所知，这是第一项提出使用未注册虹膜图像进行跨光谱虹膜匹配的框架的工作。为此，提出了三个描述符，即高斯Gabor差分（G-DoG），Gabor二值化统计图像特征（G-BSIF）和Gabor多尺度Weberface（G-MSW），以实现鲁棒的交叉光谱虹膜匹配。此外，我们探索了整个VL和NIR光谱在虹膜识别性能上的差异。实验在UTIRIS数据库上进行，该数据库包含使用同一对象的VL和NIR光谱获取的虹膜图像。实验和比较结果表明，所提出的框架实现了最新的交叉光谱匹配。此外，结果表明VL和NIR图像为虹膜图案提供了补充特征，并且它们的融合显着提高了识别性能。