基于特征块超分辨率和匹配器融合的极低分辨率虹膜识别
Very Low-Resolution Iris Recognition Via Eigen-Patch Super-Resolution and Matcher Fusion
摘要：
目前虹膜识别的研究正朝着实现更宽松的采集条件的方向发展。这对所获取图像的质量有影响，其中低分辨率是主要问题。在这里，我们评估了一个超分辨率算法用于重建虹膜图像的基础上的特征变换的局部图像补丁。每个补丁分别重建，允许更好的质量增强图像通过保留局部信息。对比度增强用于提高重建质量，匹配器融合用于提高虹膜识别性能。我们使用1,872个近红外虹膜图像的数据库来验证该系统。所提出的方法优于双线性或双三次插值，特别是在较低的分辨率下，并且两个系统的融合将下采样因子的EER推到5%以下，图像大小仅为13 x13。
英文摘要：
Current research in iris recognition is moving towards enabling more relaxed acquisition conditions. This has effects on the quality of acquired images, with low resolution being a predominant issue. Here, we evaluate a super-resolution algorithm used to reconstruct iris images based on Eigen-transformation of local image patches. Each patch is reconstructed separately, allowing better quality of enhanced images by preserving local information. Contrast enhancement is used to improve the reconstruction quality, while matcher fusion has been adopted to improve iris recognition performance. We validate the system using a database of 1,872 near-infrared iris images. The presented approach is superior to bilinear or bicubic interpolation, especially at lower resolutions, and the fusion of the two systems pushes the EER to below 5% for down-sampling factors up to a image size of only 13x13.
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