Person re-identification (Re-ID) is a significant research field that seeks to enable the identification of individuals among different surveillance cameras, making it a fundamental technology in the field of computer vision. Despite its significance, current models addressing this problem encounter difficulties when tasked with re-identifying individuals over an extended period, owing to changes in appearance such as clothing and hairstyles. We propose an innovative Re-ID network that considers facial features to enhance the model training and to improve the Re-ID performance by extracting pertinent facial information. Specifically, we filter the face images in the training set and optimize the training dataset, enabling our model to focus on essential facial features. In addition to the global stream in the training process, we introduce a distinct facial stream to train the facial features, which is subsequently merged into the overall flow at the loss function stage to enable knowledge transfer between the two branches. Experimental results on different datasets have shown that our method performs much better than the previous state-of-the-art methods in Rank-1 and mAP metrics. This proposed approach therefore presents a innovative and promising solution for the research of cloth-changing person Re-ID, and the associated code and dataset are made available on https://github.com/Titor99/FaceCC