In view of the problems of conventional palmprint recognition algorithms that are susceptible to noise interference, and poor robustness to rotation attacks, a novel palmprint recognition method is presented by using Uniform Local Binary Patterns (ULBP) and sparse representation. This method utilizes ULBP which is good at expressing image texture features and has the good rotation invariance and noise immunity characteristics to extract palmprint features. At the same time, taking into account the phenomenon that the local texture features will be lost if we directly extract palmprint features to the whole image by ULBP, this article blocks the palmprint image first and then statistics each sub-block features by ULBP. On the design of sparse classification algorithm, this article takes ULBP features of the training samples to construct a redundant dictionary, and achieves sparse decomposition of testing samples by solving the optimization problem based on l1 norm, and proposes a sparse representation classification method based on statistical average residuals to achieve recognition and classification result. The experiments demonstrate that the proposed method has the good robustness to rotation and noise, and the overall recognition rate is increased obviously. Compared with the traditional PCA and 2DPCA methods, for the database which contains 50 kinds of palmprint images, the recognition rate is increased by 8.8% and 6.8% respectively.