论文标题
通过EIGEN-PATCH超分辨率和匹配器融合，非常低分辨率的IRIS识别
Very Low-Resolution Iris Recognition Via Eigen-Patch Super-Resolution and Matcher Fusion
论文作者
论文摘要
当前在虹膜识别方面的研究正在朝着使更多放松的采集条件朝着迈进。这对获得的图像的质量产生了影响，低分辨率是主要问题。在这里，我们评估了一种基于本地图像贴片的特征转化来重建虹膜图像的超分辨率算法。每个贴片分别重建，通过保留本地信息，从而使增强图像的质量更高。对比度增强可用于提高重建质量，而采用匹配器融合来改善虹膜识别性能。我们使用1,872个近红外虹膜图像的数据库验证系统。提出的方法优于双线性或双色插值，尤其是在较低的分辨率下，两个系统的融合将EER推向低于5％的下采样因子，最高的图像大小仅为13x13。
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.
