A new palmprint recognition method based on kernel localized Fisher discriminant analysis(KLFDA) is proposed.In order to solve the singularity of the eigenvalue equation matrix in small-size-sample cases,such as image recognition,the image down-sample is first used to reduce palmprint space dimensionality.The KLFDA is applied to extract the low projection vectors.Then the kernel matrices of the training images and test images are projected onto the projection vectors to get the nonlinear localized palmprint feature vectors.Finally,the cosine distance between two feature vectors is calculated to match palmprint.The new algorithm is tested in PolyU plmprint database.The results show that compared with principal component analysis(PCA),Fisher discriminant analysis(FDA),independent component analysis(ICA),kernel principal component analysis(KPCA) and localized Fisher discriminant analysis(LFDA),the recognition rate(RR) of the new algorithm is the highest,which is 99%,and the total time for feature extraction and matching is 0.031 s,so it meets the real-time system specification.