近年来，基于线性判别分析(LDA)的图像模式识别方法研究越来越受到人们的关注。然而LDA方法自身存在的小样本难题，极大的影响了样本集特征矩阵的获取。研究者随后提出的2维线性分析(2D-LDA)在一定程度上解决了这个问题。在传统2D-LDA基础上，提出一种改进的2维线性分析方法——2D-PLDA，该方法通过对样本集进行预分类，使得散布矩阵更加合理；在此基础上将2D-PLDA和离散小波相结合，应用于虹膜识别中。实验结果证明，该算法在识别精度和计算复杂度等方面均较传统LDA和2D-LDA方法有很大的改进，同时采用小波的不同子带作为输入空间也在一定程度上增加了算法的鲁棒性。

In the last few years linear Discriminant Analysis(LDA)become more popular.But small sample size problem(SSSP) is always the biggest problem to perform it.In order to overcome this shortcoming,2D-LDA is proposed. We improve 2D-PLDA by Pre-Clustering,which can make the distribution matrix precisely;then a new iris recognition arithmetic is proposed which is combined with 2D-PLDA and wavelet transform.In the experiment,preprocessing was performed at first,then we extract the feature vector from the known class sample by 2D-PLDA and wavelet.In the validation step,we use Euclidean distance and Hamming distance to find the K-nearest neighbor to decide which class the unknown sample belongs to.From the experiment result,we can conclude that,the proposed arithmetic can achieve higher recognition rate than traditional LDA and 2D-LDA, also the new arithmetic is simple in calculation. We use the different subbands as the input space of 2D-PLDA,so the robustness is enhanced.