为了更加完备地描述虹膜的纹理特征，利用虹膜图像灰度信息提取局部特征点、局部纹理方向和局部纹理的亮暗变化三种特征共同构成纹理的特征空间，克服了之前的多数虹膜识别算法提取单一特征易受干扰影响的局限性。然后，通过设计的模糊推理规则进行模式的分类，这种分段线性分类器的设计提高了算法线性分类的能力。分别在两个图库上进行了实验，识别率分别达到99.41%和99.67%，实验数据表明：结合多种特征能较好的反映虹膜的纹理变化特征，提高了虹膜识别的正确率，使算法具有非常优越的识别性能。

To get more representative iris features, three features, local feature point, local texture direction and changes between brightness and darkness of local texture are extracted through the gray-scale of iris images, which can depict the feature space of texture more fully and overcome the limitation of most previous algorithms which extract only susceptible single feature. Then patterns are categorized by the designed fuzzy inference regulation. The design of this piecewise linear classifier enhances the ability of linear classification of the algorithm. Experiments are implemented in two databases, respectively. The correct recognition rate is 99.41% and 99.67%, which demonstrate that many kinds of features can represent the variation details in the iris patterns properly. Therefore, the correctness is improved and the algorithm wins predominant recognition performance.