In order to extract the texture features of palm vein image and improve the recognition rate effectively, a texture feature extraction method based on joint Gabor wavelet and neighbor binary pattern (NBP) is proposed. Considering the difference of vein thickness and extension direction in venous structure, this method obtains multiple Gabor-magnitude features by convoluting the region of interest of the palm vein image with Gabor wavelet of four scales and four directions. And the Gabor scale-mean pattern (GSP) is obtained by averaging four different scales. The GSP neighbor binary pattern (GSPNBP) is extracted from each GSP block using the NBP description operator. Then the concatenation of the coding sequences of these multi-scale and multi-direction GSPNBP regions is used as the feature vector of the palm vein. Finally, the similarity of the two vein images is calculated by Hamming distance of the feature vectors, and the experiments are carried out in the PolyU and self-built database, respectively. Experimental results show that the highest recognition rate of this algorithm can be reached to 99.7935% and 99.3965%, respectively, and the recognition time is less than 1s, which effectively enhances the robustness of the algorithm.