基于ROI归一化的ICA/PCA虹膜识别方法

ICA/PCA Iris Recognition Method Based on ROI Normalization

针对虹膜识别中使用传统归一化方法时,无法突出主要纹理信息,并且由于提取数据量大导致特征提取阶段计算复杂的缺陷,提出一种基于感兴趣区域(ROI)的虹膜归一化方法,先利用主成分分析(PCA)方法提取主元,实现进一步的降维和去噪,再使用独立分量分析(ICA)进行训练形成ICA/PCA虹膜特征提取算法,最后使用余弦距分类器对待测样本进行分类.实验结果表明,该方法识别率为98.11%,识别时间小于70ms。

In view of the shortcomings of redundant data leading to computational complexity of feature extraction after traditional normalization in iris recognition and weakening of main texture information,we introduced the ROI-based iris normalization means and used PCA to extract principal component to reduce the dimension and remove noise further,then trained feature vectors using ICA to make ICA /PCA iris feature extraction algorithm;finally used cosine distance classifier to identify testing samples.Experimental recognition rate was reached up to 98.11%,and recognition time was less than 70 ms.