Specialized Re-Ranking: A Novel Retrieval-Verification Framework for Cloth Changing Person Re-Identification

TL;DR

This paper introduces a new retrieval-verification framework for cloth-changing person re-identification, improving accuracy by combining fast retrieval with detailed local comparison, especially effective in complex scenarios.

Contribution

It proposes a novel retrieval-verification framework with an innovative ranking strategy, enhancing re-identification performance under clothing changes and similar-looking confusions.

Findings

Significant improvement over state-of-the-art methods on synthetic datasets.

Effective in real-world scenarios with clothing variations.

Robustness demonstrated through comprehensive experiments.

Abstract

Cloth changing person re-identification(Re-ID) can work under more complicated scenarios with higher security than normal Re-ID and biometric techniques and is therefore extremely valuable in applications. Meanwhile, higher flexibility in appearance always leads to more similar-looking confusing images, which is the weakness of the widely used retrieval methods. In this work, we shed light on how to handle these similar images. Specifically, we propose a novel retrieval-verification framework. Given an image, the retrieval module can search for similar images quickly. Our proposed verification network will then compare the input image and the candidate images by contrasting those local details and give a similarity score. An innovative ranking strategy is also introduced to take a good balance between retrieval and verification results. Comprehensive experiments are conducted to show the effectiveness of our framework and its capability in improving the state-of-the-art methods remarkably on both synthetic and realistic datasets.