中文摘要：

一个改进 Daugman 虹识别算法在这份报纸被提供，它在二表达方面：为虹本地化的改进和为虹编码并且匹配算法的改进。在步 1，学生的本地化和形状粗略地在虹图象被决定，它被用作优先的知识快速定位虹的内部、外部的边界从对好规模不平。在学生的眼皮，睫毛区域和点自动地被检测并且搬迁了改进本地化精确性。在步 2，从剩余睫毛的可能的噪音被作为一本参考书选择一个纯虹区域并且使一个确认判断象素明智进一步过滤。而且，为每个象素的确认标志被介绍进编码的虹和匹配的计算，作为结果，虹识别的拒绝率被减少。与 Daugman 算法相比，虹识别测试在上收集了人的眼睛图象证明我们的建议算法两个都穿上明显的改进增加速度并且减少拒绝率。

英文摘要：

An improved Daugman iris recognition algorithm is provided in this paper, which embodies in two aspects： 1 Improvement for iris localization and 2 The improvement for both iris encoding and matching algorithms. In Step 1, the localization and shape of the pupil are roughly determined in iris image, which is used as prior knowledge to quickly locate the inner and outer boundary of iris from rough to fine scale. Eyelids, eyelashes areas and the spot in the pupil are automatically detected and removed to improve the localization accuracy. In Step 2, the possible noise from residual eyelashes is further filtered by selecting a ＂pure＂ iris area as a reference and making a validation judgment pixel-wise. Furthermore, the validation flag for each pixel is introduced into the iris encoding and matching computation, as a result, the rejection rate of iris recognition is reduced. Compared with Daugman algorithm, iris recognition test on collected human eye images shows that our proposed algorithm has an obvious improvement both on boosting the speed and reducing the rejection rate.