A method based on difference guide and feature self-enhancement for clothes-changing person re-identification

Abstract

Abstract Due to the effect of clothing change on person re-identification models, some researchers have car-ried out in-depth studies on clothes-changing person re-identification(CC-ReID). However, there are some problem of the loss of edge identity information in the semantic guidance process in current methods. In this work, we propose a dual-stream network model, named GFSAnet, which consists of both global and face streams. This model is capable of retaining edge identity information while reinforcing the weight of fine-grained discriminative information. Firstly, in the global stream, we de-sign a difference guide model (DGM) and a feature self-augmentation model (FSAM). The differential features are learned through the difference guide module to preserve the edge identity information of the boundary between background and foreground, while the weights of the local information in the network are optimized through the feature self-augmentation module. Secondly, in the face stream, the multi-scale structure design of pyramid residual network is used to learn the facial features fusing coarse and fine granularity. Finally, the contributions of global and facial features are dynamically adjusted to work together in the inference by setting the hyperparameter α. Extensive experiments show that the method in this paper achieves better performance on the PRCC, Celeb-ReID and Celeb-Light datasets.