为克服小波变换和Gabor滤波器提取虹膜特征时小波基函数固定和Gabor滤波器参数需优化选择的问题,提出了一种基于经验模态分解（EMD）和奇异值分解（SVD）的虹膜特征提取方法.首先,对预处理后的虹膜图像进行EMD,将获得的一系列固有模态函数和残差分量构成初始矩阵;然后,对该矩阵进行SVD,以其奇异值作为虹膜特征向量;最后,利用ModestAdaBoost分类器进行识别.实验结果表明,该方法提取的特征向量维数少,识别率高,虹膜特征提取和匹配时间复杂度低.

During the extraction of iris features by the wavelet transform and Gabor filter,the wavelet basis function is fixed and Gabor filter parameters should be optimized.In order to solve these problems,a new extraction method of iris features is proposed based on the empirical mode decomposition（EMD） and the singular value decomposition（SVD）.In this method,first,the preprocessed iris image is decomposed via EMD,and a series of intrinsic mode functions and residual components are obtained to construct the initial feature vector matrixes.Then,the matrixes are decomposed via SVD,and the corresponding singular value is taken as the iris feature vector.Finally,iris features are identified by using a Modest AdaBoost classifier.Experimental results show that the proposed method helps to obtain low-dimension feature vector with higher recognition rate and lower time complexity of feature extraction and matching.