基于IMF奇异值分解和SVM的虹膜识别方法

提出了基于固有模态函数奇异值分解和支持向量机的虹膜识别方法．用一维经验模式分解对按行展开的虹膜数据进行分解，将得到的若于个IMF形成初始矩阵，然后对该矩阵进行奇异值分解，提取其奇异值作为虹膜特征向量输入支持向量机进行分类识别．与传统的Gabor小波特征提取方法比较，本文方法解决了滤波器参数繁杂同题且在编码长度和时间方面有明显的改进．试验结果表明，本文方法能有效地应用于身份鉴别系统中．

Iris recognition based on singular value decomposition for intrinsic mode and support vector machines

A new method for iris feature extraction and recognition was proposed in this paper.Firstly,the empirical mode decomposition method is used to decompose the preprocessed iris signal into a number of intrinsic mode function components,from which the initial feature vector matrixes are formed.Secondly,by applying the singular value decomposition technique to the initial vector matrixes,the singular values are obtained.Finally,the singular values serve as the iris feature vectors to be input to the support vector machine and are identified by the output of the classifier.Compared with that of Gabor method,the size of an iris code and the processing time of the feature extraction were significantly reduced.The experiment indicates that the approach could provide a good result for the iris recognition.