支持向量机虹膜识别算法研究

Research on SVM Iris Recognition Algorithm

提出一种基于极值加权平均分数维特征提取和支持向量机分类器识别的虹膜识别方法。利用形态学和圆形边缘检测算子定位虹膜,并将虹膜纹理映射到极坐标空间;定义了一种新的图像分数维——极值加权平均分数维,用于提取虹膜特征;利用支持向量机分类器对虹膜特征矩阵进行匹配识别。试验表明,基于极值加权平均分数维特征提取和支持向量机分类器识别的虹膜识别系统识别率高,速度快。

Proposes an iris recognition algorithm based on fractal dimension in feature extraction and support vector machine classification. The iris is located by a circle detector according to mathematical morphology, and mapped to polar coordinates space. Then a new definition of fractal dimension, extreme value weighted mean fractal dimension is given, and with little feature matrix is extracted. At last, a SVM classifier is used in matching stage. The experimental results show that the system based on extreme value weighted mean fractal dimension in feature extraction and SVM classification is precise and rapid.