独立成分分析和支持向量机的虹膜识别方法

为了提高虹膜识别的性能,提出了一种新的虹膜识别方法。该方法采用独立成分分析获取虹膜高阶统计信息,并将输入模式空间映射到相应的独立成分空间,然后在该独立成分空间中,利用支持向量机的泛化特性构造最优分类超平面。通过CASIA虹膜数据库的仿真实验,该方法降低了特征空间维数,具有较高的正确识别率。特别是对高斯核,取得了98.61%的正确识别率,较相异度函数和最近特征线方法分别高6.48%和4.54%,同时也提高了算法的鲁棒性和灵活性。

Iris Recognition Method Based on Independent Component Analysis and Support Vector Machine

In order to improve the performance of iris recognition, a novel iris recognition method is presented. In this method the independent component analysis is used to obtain iris high order statistic information and mapped the input mode space into the corresponding independent component space. Then the maximal hyperplane is constructed in the independent component space using the generalization of the support vector machine. Numerical simulation based on the CASIA iris database shows that the proposed method can reduce the dimension of the feature space and has higher correct classification rate. Especially, though using Gauss kernel, the rate of correct recognition reaches 98.61% which is increased 6.48% and 4.54% respectively comparing with dissimilarity functions and the nearest feature line method, while improving robustness and flexibility of iris recognition.