In order to solve the problem that the local binary pattern (LBP) is easily affected by random noise and edge points on the image, and the threshold cannot be automatically selected when the local binary mode describes the texture features of the image, resulting in poor robustness, a palmprint recognition method based on weighted adaptive multiple uniform local binary pattern (WAMULBP) and twodimensional principal component analysis (2DPCA) is proposed. Firstly, the histogram equalization (HE) is used to perform pretreatment of the palmprint region of interest (ROI) image to reduce the impact of the illumination change during imaging on the final palmprint recognition success rate. Secondly, the preprocessed image is divided into sizes Uniform subblocks and use adaptive multiple uniform local binary pattern (AMULBP) algorithm to obtain texture feature histograms and weights of each subblock. Finally, the texture feature histogram of each subblock is multiplied and concatenated to obtain the final texture feature histogram, after the 2DPCA dimension reduction, the Euclidean distance discriminant method is used for palmprint recognition. Comparing experiments on Hong Kong Polytechnic University PolyU database, Tongji and IITD noncontact database, selfbuilt noncontact database and their noise database. The lowest equivalent error rates are 1879 0%, 2019 2%, 2184 9%, 2663 2%, 4380 3%, 4730 1%, 5005 0% and 5223 7% and the recognition time is within 1 s. Compared with other algorithms, the recognition accuracy and robustness are effectively improved while ensuring realtime performance.