Specialized Re-Ranking: A Novel Retrieval-Verification Framework for Cloth Changing Person Re-Identification
Renjie Zhang, Yu Fang, Huaxin Song, Fangbin Wan, Yanwei Fu, Hirokazu, Kato, and Yang Wu

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…
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Taxonomy
TopicsFace recognition and analysis · Video Surveillance and Tracking Methods · Biometric Identification and Security
