基于毯子维和缺项的虹膜特征提取算法

虹膜特征提取是虹膜识别的一个关键环节。文中提出一种毯子维和缺项相结合的虹膜特征提取算法。利用虹膜纹理的自相似和丰富的变化细节所具有的分形几何特性，采用纵向扩展毯子维表达不同分辨能力下虹膜纹理的变化及其辐射排列特点。通过引入缺项进一步提取不同纹理和分形表现却具有相同分形维数的虹膜特征。两者的结合能够更加全面地反映虹膜纹理的细腻变化。将归一化虹膜图像灰度值的毯子维及缺项用于虹膜分类，提高对虹膜的分类能力。针对CASIA-IrisV3-Interval数据库的仿真结果表明，纵向扩展毯子维结合缺项能够有效、快速提取虹膜的纹理信息，所获得的特征具有高的虹膜识别性能。

Iris Feature Extraction Algorithm Using Vertically Expanded Blanket Dimension and Lacunarity

Iris feature extraction is important in iris recognition. An iris feature extraction algorithm is proposed by using combination of blanket dimension and lacunarity. Because Human iris texture is characterized by fractal geometry due to its rich self-similarity and abundant variation, vertically expanded blanket dimension is employed to represent iris texture variation and radial pattern at different resolution levels. Lacunarity is introduced to extract iris features that have different texture and fractal patterns but have same fractal dimension value. The combination of blanket dimension and lacunarity in iris feature extraction can embody the minute change of texture information comprehensively, and improve the capacity of iris classification. The experimental results on the CASIA-IrisV3-Interval iris database show that the combination of blanket dimension and lacunarity can extract iris textural features accurately and effectively, and high performance for iris recognition is achieved by using those features.