为了提高虹膜识别的正确率，提出了利用主成分分析(PCA)与独立成分分析(ICA)相结合的方法，来对虹膜进行识别的方法。用该方法进行虹膜识别时，首先对预处理后的虹膜图像,利用PCA算法进行去二阶相关和降维处理；然后再进行ICA训练。ICA训练采用了以下两种方法：方法1，将参与ICA训练的图像看作是随机变量，而将图像中的像素值看作是随机实验结果，ICA训练后即可得到相互独立的ICA虹膜基图像；方法2，将图像中的像素值看作是随机变量，而将图像看作是随机实验结果，ICA训练后即得到相互独立的ICA系数。采用CASIA虹膜数据库进行的试验结果表明,基于PCA和ICA的虹膜识别算法在两种训练方式下的正确识别率分别达到98.89％和98.33％。

A new iris recognition algorithm, based on PCA and ICA, is proposed in this paper. Firstly, PCA was applied to the iris images in order to reduce dimension and second order correlation, then ICA was applied to train iris images. In our algorithm, ICA was performed on iris images in the CASIA database under two different architectures, of which one treated the image as random variables and the pixels as outcomes, while the other treated the pixels as random variables and the images as outcomes. The first architecture found spatially independent basis images for the iris. The second architecture used ICA to find a representation in which the coefficients used to code images were statistically independent. No matter which architecture we used to train the iris images, the proposed algorithm was effective.