Center for Biometrics and Security Research, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Haidian District, Beijing, PR China.
IEEE Trans Pattern Anal Mach Intell. 2009 Dec;31(12):2211-26. doi: 10.1109/TPAMI.2008.240.
Images of a human iris contain rich texture information useful for identity authentication. A key and still open issue in iris recognition is how best to represent such textural information using a compact set of features (iris features). In this paper, we propose using ordinal measures for iris feature representation with the objective of characterizing qualitative relationships between iris regions rather than precise measurements of iris image structures. Such a representation may lose some image-specific information, but it achieves a good trade-off between distinctiveness and robustness. We show that ordinal measures are intrinsic features of iris patterns and largely invariant to illumination changes. Moreover, compactness and low computational complexity of ordinal measures enable highly efficient iris recognition. Ordinal measures are a general concept useful for image analysis and many variants can be derived for ordinal feature extraction. In this paper, we develop multilobe differential filters to compute ordinal measures with flexible intralobe and interlobe parameters such as location, scale, orientation, and distance. Experimental results on three public iris image databases demonstrate the effectiveness of the proposed ordinal feature models.
人眼虹膜图像包含丰富的纹理信息，可用于身份验证。虹膜识别中的一个关键且尚未解决的问题是，如何使用一组紧凑的特征（虹膜特征）来最佳地表示这种纹理信息。在本文中，我们提出使用有序度量来进行虹膜特征表示，其目的是描述虹膜区域之间的定性关系，而不是对虹膜图像结构进行精确测量。这种表示方法可能会丢失一些特定于图像的信息，但它在区分度和稳健性之间取得了很好的平衡。我们表明，有序度量是虹膜模式的固有特征，并且在很大程度上不受光照变化的影响。此外，有序度量的紧凑性和低计算复杂度使得高效的虹膜识别成为可能。有序度量是一种用于图像分析的通用概念，并且可以为有序特征提取导出许多变体。在本文中，我们开发了多叶微分滤波器，以计算具有灵活的叶内和叶间参数（例如位置、比例、方向和距离）的有序度量。在三个公共虹膜图像数据库上的实验结果表明了所提出的有序特征模型的有效性。
