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.