Abstract: In order to fully exploit the features of any input image for person re-identification (Re-ID), part-based convolutional baseline (PCB) was proposed to employ a uniform partition for any input image and a refined part pooling (RPP) method is followed for enhanced within-part consistency. In order to further improve the performance of Person Re-ID, this paper proposes a weighted PCB algorithm, which combines the global feature and local part-based features in a weighted form. Experiments show that the proposed algorithm is better than other weighted methods. Experiments over Market1501 and DukeMTMC-Reid show that the proposed algorithm can achieve better performance in both the Rank1 accuracy and the mean average accuracy (mAP). Compared with the PCB+RPP algorithm, the proposed algorithm provides a margin of 0.8% and 4.5% over Market1501 for Rank1 and mAP, respectively. For the dataset of DukeMTMC-Reid, it improves PCB by 5.5% in Rank1 accuracy and by about 7% in mAP.