中文摘要：

传统的虹膜识别方法主要提取和匹配局部区域特征，忽略了距离较远区域（即非局部区域）特征之间的相关性。基于序特征的方法通过高斯低通滤波器提取区域的平均灰度值并对不同区域进行大小比较，但是这种方法并不适用于用概率密度描述区域统计特性的情况。本文提出一种新颖的虹膜识别方法解决传统方法的不足。该方法在用空间-相位联合分布表示局部区域纹理特征的基础上，通过将位于距离较远图像区域的特征进行连接得到非局部区域关联描述子表达区域之间的关联特性。论文着重研究了两区域和三区域关联对虹膜识别性能的影响。在虹膜匹配时，考虑非局部区域关联描述子的有效性以排除遮挡、高亮和噪声等干扰因素的影响，允许非局部区域关联描述子进行整体微小平移以建模虹膜纹理的非刚性形变，最后用一种鲁棒的扩散直方图距离比较关联描述子之间的差异。论文在三个公开的虹膜数据库中进行了虹膜验证和虹膜识别实验，结果表明所提出的方法在性能上优于同类方法。

英文摘要：

The traditional iris recognition methods focused on extracting and matching local region characteristics,yet failing to consider the correlation of long-distance regions（non-local regions）.The ordinal measures-based methods extracted the average gray-level values of non-local regions by Gaussan filtering and then ranked them,which unfortunately were not applicable to situa-tions where regions are described by probability distributions .This paper presents a novel recognition method for tackling these shortcomings .The proposed method represents the texture characteristics of local regions using spatio-phase joint probability distribu-tions,and further explores the associating relations between them .We concatenate the features of regions at varying positions to ob-tain the non-local associating descriptors for modeling their relations,among which we primarily study the recognition performance by associating two or three regions .During iris matching process,we consider the effectiveness of non-local region associating de-scriptors to exclude the effects of occlusion,highlights,noise and etc,and allow small translation of the associating descriptors to model local deformation of iris texture;finally we adopt a robust diffusion distance between histograms for descriptors comparison . We conduct experiments for iris verification and iris identification on three public databases,and experiments demonstrate that the proposed method is superior to the state-of-the-arts .