基于特征加权FDCT和模糊最小二乘支持向量机的虹膜识别算法  

Iris recognition algorithm based on feature weighting fast discrete Curvelet transform and fuzzy LS-SVM

作　　者：何振红[1] 

机构地区：[1]甘肃民族师范学院计算机科学系,甘肃合作747000

出　　处：《电信科学》2016年第3期92-98,共7页Telecommunications Science

摘　　要：为了克服小波变换在二维空间分析的缺陷,提出了基于快速离散曲波(Curvelet)变换的虹膜识别改进算法。利用能有效捕捉图像边缘信息的Curvelet变换对虹膜图像进行分解,提取低频子带系数矩阵的均值方差和高频子带能量,然后根据不同子带特征的分类能力不同,对各子带特征的离散度进行加权,为分类能力较强的特征向量赋予较大权值,构成虹膜图像的特征向量。利用最优二叉树多类模糊最小二乘支持向量机分类器进行分类与识别。仿真实验结果表明,该算法具有较高的识别性能,具有可行性。In order to overcome the weakness of wavelet transform in two dimensional spatial analysis, an improved algorithm based on fast discrete Curvelet transform for iris recognition was proposed. Curvelet transform which can effectively capture the image edge information was introduced to decompose iris image. Mean and variance of low frequency sub-band coefficients and the energy of high frequency sub-band were extracted. Then the feature vectors were weighted according to the difference of classification ability of sub-band feature. Fuzzy least square support vector machine with optimal binary tree was developed to implement classification and recognition. The simulation results show that the proposed algorithm has higher recognition performance than the present method.

关 键 词：虹膜识别 特征加权 快速离散曲波变换 模糊最小二乘支持向量机 最优二叉树 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]