Identity-Sensitive Knowledge Propagation for Cloth-Changing Person Re-identification

TL;DR

This paper introduces DeSKPro, a novel framework for cloth-changing person re-identification that leverages identity-sensitive cues from facial features and human parsing, achieving superior performance without high computational costs.

Contribution

The paper proposes a new identity-sensitive knowledge propagation framework that eliminates clothing distraction and restores facial details, improving CC-ReID accuracy efficiently.

Findings

Outperforms state-of-the-art methods significantly

Effectively restores facial details from low-resolution images

Reduces computational costs by removing auxiliary modules after training

Abstract

Cloth-changing person re-identification (CC-ReID), which aims to match person identities under clothing changes, is a new rising research topic in recent years. However, typical biometrics-based CC-ReID methods often require cumbersome pose or body part estimators to learn cloth-irrelevant features from human biometric traits, which comes with high computational costs. Besides, the performance is significantly limited due to the resolution degradation of surveillance images. To address the above limitations, we propose an effective Identity-Sensitive Knowledge Propagation framework (DeSKPro) for CC-ReID. Specifically, a Cloth-irrelevant Spatial Attention module is introduced to eliminate the distraction of clothing appearance by acquiring knowledge from the human parsing module. To mitigate the resolution degradation issue and mine identity-sensitive cues from human faces, we propose to restore the missing facial details using prior facial knowledge, which is then propagated to a smaller network. After training, the extra computations for human parsing or face restoration are no longer required. Extensive experiments show that our framework outperforms state-of-the-art methods by a large margin. Our code is available at https://github.com/KimbingNg/DeskPro.