  Cloth-changing person re-identification (CC-ReID) aims to retrieve specific
pedestrians in a cloth-changing scenario. Its main challenge is to disentangle
the clothing-related and clothing-unrelated features. Most existing approaches
force the model to learn clothing-unrelated features by changing the color of
the clothes. However, due to the lack of ground truth, these methods inevitably
introduce noise, which destroys the discriminative features and leads to an
uncontrollable disentanglement process. In this paper, we propose a new person
re-identification network called features reconstruction disentanglement ReID
(FRD-ReID), which can controllably decouple the clothing-unrelated and
clothing-related features. Specifically, we first introduce the human parsing
mask as the ground truth of the reconstruction process. At the same time, we
propose the far away attention (FAA) mechanism and the person contour attention
(PCA) mechanism for clothing-unrelated features and pedestrian contour features
to improve the feature reconstruction efficiency. In the testing phase, we
directly discard the clothing-related features for inference,which leads to a
controllable disentanglement process. We conducted extensive experiments on the
PRCC, LTCC, and Vc-Clothes datasets and demonstrated that our method
outperforms existing state-of-the-art methods.
