Features Reconstruction Disentanglement Cloth-Changing Person Re-Identification

TL;DR

This paper introduces FRD-ReID, a novel cloth-changing person re-identification method that effectively disentangles clothing-related and unrelated features using reconstruction guided by human parsing masks.

Contribution

The proposed FRD-ReID network uses human parsing masks and attention mechanisms to controllably disentangle clothing features, improving cloth-changing person re-identification performance.

Findings

Outperforms state-of-the-art methods on PRCC, LTCC, and Vc-Clothes datasets.

Effectively disentangles clothing-related and unrelated features.

Demonstrates controllable feature removal during inference.

Abstract

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.