Abstract: Images of a same person have extreme variation due to viewpoint,pose,occlusion and detection error,which are major challenges in person re-identification.For the purpose of improving the accuracy of person re-identification under person’s pose changing,a correlation channel-wise based part aligned representations(CCPAR)for person re-identification is proposed.Firstly,person images are input into two sub-networks to obtain person appearance and part features respectively.Secondly,a correlation channel-wise module(CCM)is de-signed for optimizing the channels weights of part features.The CCM mines the correlation between channels of part features.Finally,appearance features and optimized part features are fused by bilinear pooling.Experiments on three large scale datasets show that the CCM can enhance part features.On Market-1501,DukeMTMC-reID and CUHK03 datasets,Rank-1/mAP of CCPAR reaches 93.9%/90.6%,87.6%/83.3%and 70.4%/72.8%,which is superior to other existing methods.