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Iris Super-Resolution Using Iterative Neighbor Embedding

机译：虹膜超级分辨率使用迭代邻居嵌入

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Iris recognition research is heading towards enabling more relaxed acquisition conditions. This has effects on the quality and resolution of acquired images, severely affecting the accuracy of recognition systems if not tackled appropriately. In this paper, we evaluate a super-resolution algorithm used to reconstruct iris images based on iterative neighbor embedding of local image patches which tries to represent input low-resolution patches while preserving the geometry of the original high-resolution space. To this end, the geometry of the low-and high-resolution manifolds are jointly considered during the reconstruction process. We validate the system with a database of 1,872 near-infrared iris images, while fusion of two iris comparators has been adopted to improve recognition performance. The presented approach is substantially superior to bilinear/bicubic interpolations at very low resolutions, and it also outperforms a previous PCA-based iris reconstruction approach which only considers the geometry of the low-resolution manifold during the reconstruction process.
机译：虹膜识别研究正在朝向实现更多放松的收购条件。这对所获得的图像的质量和分辨率产生影响，如果没有适当地解决识别系统的准确性，则严重影响识别系统的准确性。在本文中，我们评估用于基于局部图像贴片的迭代邻居嵌入来重建虹膜图像的超分辨率算法，该算法试图在保留原始高分辨率空间的几何形状的同时表示输入的低分辨率贴片。为此，在重建过程中共同考虑了低压和高分辨率歧管的几何形状。我们使用1,872近红外虹膜图像的数据库验证系统，同时采用了两个虹膜比较器的融合来提高识别性能。所提出的方法在非常低的分辨率下基本上优于双线性/双立插，并且它也优于以前的基于PCA的虹膜重建方法，该方法仅考虑在重建过程中的低分辨率歧管的几何形状。

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