基于支持向量机分数等级融合的虹膜识别方法

提出了一种使用支持向量机（Support Vector Machine，SVM）的分数等级融合的虹膜识别方法。通过对虹膜纹理采用小波包分解，选择最高能量区域和次高能量区域提取特征向量，与注册入库的虹膜特征向量计算出海明距离。最后融合两个海明距离输入SVM进行识别。该方法减少输入支持向量机的维数。实验结果表明，该法提高了识别率，能够有效地应用到身份鉴别系统中。

Iris recognition based on score level fusion by using SVM

In this paper,an iris recognition method using wavelet packet transformation and based on score level fusion by using SVM is proposed.First wavelet packet transformation is used to decomposition normalized iris image for 2 levels,and the feature vectors are selected from the area with the highest energy values and the second highest,then two calculated hamming distance are input to SVM in order to reduce the dimension of SVM and improve veracity.The experiment indicates that proposed approach provides a good result for the iris recognition.