The key to success of person re-identification(re-id) is extracting the discriminative person features. Various part-level feature extraction methods are proposed to capture local person features for re-id. A prerequisite of part feature extraction is that each part should be well located. We believe that ID predictions in different parts of the same image should be consistent. Instead of using the external dataset and pose estimator for guiding, we propose a re-id model with Part Prediction Alignment (PPA), which aims at aligning the predicted distributions between each part. Due to the global feature and local feature contains different spacial information, we consider that the combination of two sides will further improve the detection effect. Therefore, in this paper we adopt the teacher–student training strategy based on PPA for global–local feature extraction, and the global feature extraction branch as a teacher to guide the training of local feature branch. Experimental results on Market-1501, DukeMTMC-reID and CUHK03 (including CUHK03_Detected and CUHK03_Labeled) datasets confirm the effectiveness of our proposal, we achieve Rank1 with 92.4%, 85.1%, 65.5%, 69.2% on Market-1501, DukeMTMC-reID, CUHK03_Detected and CUHK03_Labeled, respectively.

人员重新识别（re-id）成功的关键是提取具有区别性的人员特征。提出了各种局部特征提取方法来捕获本地人特征以进行重新识别。零件特征提取的前提条件是每个零件都应放置在适当的位置。我们认为，同一张图片的不同部分中的ID预测应该是一致的。代替使用外部数据集和姿态估计器进行指导，我们提出了带有零件预测对齐（PPA）的re-id模型，该模型旨在对齐每个零件之间的预测分布。由于全局特征和局部特征包含不同的空间信息，因此我们认为将两者结合可以进一步提高检测效果。所以，在本文中，我们采用基于PPA的师生训练策略进行全局-局部特征提取，并以全局特征提取分支为教师来指导局部特征分支的训练。在Market-1501，DukeMTMC-reID和CUHK03（包括CUHK03_Detected和CUHK03_Labeled）数据集上的实验结果证实了我们的建议的有效性，我们在Market-1501，DukeMTMC-reID， CUHK03_Detected和CUHK03_Labeled。