In this study, we propose a person re-identification model based on view information embedding. In particular, a pose-sensitive embedding (PSE) network structure is optimized based on the perspective towards characteristics of pedestrian images. First, the fusion part of the PSE feature vector is changed from feature fusion into the concatenation of the feature vectors of three view units, which is considerably reasonable for utilizing different view feature spaces. Second, the view units are separated from the shallow blocks-3 of the skeleton network, which improves the difference of the view feature space. Finally, we design a depthwise separable module based on the improved depth separable convolution to extract features of perspective units, preventing the model parameters from being considerably large and improving the network nonlinearity. The results of the validation experiments conducted using the Market1501, Duke-MTMC-reID and MARS datasets demonstrate that the proposed method can achieve a better recognition accuracy when compared with several advanced algorithms.