Weber local descriptor (WLD) is an effective image feature descriptor. However, the differential excitation and gradient orientation, which are two components of WLD, can not accurately describe the difference of local image blocks and the orientation of palm lines, so the performance of palmprint recognition based on WLD features is not high. In order to improve palmprint recognition performance, multi-scale Gabor orientation Weber local descriptors are proposed in view of the rich line features of palmprint images. First, multi-scale Gabor filter is used to filter the palmprint image to generate multi-scale energy maps and orientation maps. Then, the differential excitation is calculated based on energy maps. Finally, the histogram features are constructed based on multi-scale differential excitation maps and orientation maps, and the feature vectors from different scales are then concatenated to produce the final feature set of a palmprint image. The experiments on PolyU, PolyU Multi-spectral and CASIA palmprint databases show that the proposed method can achieve higher identification rate and lower equal error rate compared with some existing palmprint recognition methods.