GEFF: Improving Any Clothes-Changing Person ReID Model Using Gallery Enrichment with Face Features

Daniel Arkushin*, Bar Cohen, Shmuel Peleg, Ohad Fried

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In the Clothes-Changing Re-Identification (CC-ReID) problem, given a query sample of a person, the goal is to determine the correct identity based on a labeled gallery in which the person appears in different clothes. Several models tackle this challenge by extracting clothes-independent features. However, the performance of these models is still lower for the clothes-changing setting compared to the same-clothes setting in which the person appears with the same clothes in the labeled gallery. As clothing-related features are often dominant features in the data, we propose a new process we call Gallery Enrichment, to utilize these features. In this process, we enrich the original gallery by adding to it query samples based on their face features, using an unsupervised algorithm. Additionally, we show that combining ReID and face feature extraction modules along- side an enriched gallery results in a more accurate ReID model, even for query samples with new outfits that do not include faces. Moreover, we claim that existing CC-ReID benchmarks do not fully represent real-world scenarios, and propose a new video CC-ReID dataset called 42Street, based on a theater play that includes crowded scenes and numerous clothes changes. When applied to multiple ReID models, our method (GEFF) achieves an average improvement of 33.5% and 6. 7% in the Top-1 clothes-changing metric on the PRCC and LTCC benchmarks. Combined with the latest ReID models, our method achieves new SOTA results on the PRCC, LTCC, CCVID, LaST and VC-Clothes benchmarks and the proposed 42Street dataset.

Original languageEnglish
Title of host publicationProceedings - 2024 IEEE Winter Conference on Applications of Computer Vision Workshops, WACVW 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages143-153
Number of pages11
ISBN (Electronic)9798350370287
DOIs
StatePublished - 2024
Event2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2024 - Waikoloa, United States
Duration: 4 Jan 20248 Jan 2024

Publication series

NameProceedings - 2024 IEEE Winter Conference on Applications of Computer Vision Workshops, WACVW 2024

Conference

Conference2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2024
Country/TerritoryUnited States
CityWaikoloa
Period4/01/248/01/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

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