A detection and classification method for iris pigment block
Xiaona Liu · Shenyang Gongye Daxue xuebao · 2014
In order to solve the problem that the global texture feature extraction methods in the existing iris recognition system ignore the information of texture types,a detection and classification method for iris pigment blocks based on global texture was proposed. The initial location of probably existing regions of pigment blocks in the iris images was realized with a gray cluster method. According to the gray spatial distribution characteristics of two pigment blocks including plaques and crypts,a set of region feature parameters were defined as the classification feature vector. In addition,the detection and classification of iris plaques and crypts were realized by a support vector machine. The detection accuracy for crypts and plaques of images in the gallery are 99. 2% and 86. 5%,respectively. M oreover,the detection accuracy of iris images without any feature textures is 87. 2%. The experimental results showthat the proposed method has higher detection accuracy,and can meet the requirement of texture feature extraction in the iris recognition system.