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In the Clothes-Changing Re-Identification (CC-ReID) problem, given a querysample of a person, the goal is to determine the correct identity based on alabeled gallery in which the person appears in different clothes. Severalmodels tackle this challenge by extracting clothes-independent features.However, the performance of these models is still lower for theclothes-changing setting compared to the same-clothes setting in which theperson appears with the same clothes in the labeled gallery. Asclothing-related features are often dominant features in the data, we propose anew process we call Gallery Enrichment, to utilize these features. In thisprocess, we enrich the original gallery by adding to it query samples based ontheir face features, using an unsupervised algorithm. Additionally, we showthat combining ReID and face feature extraction modules alongside an enrichedgallery results in a more accurate ReID model, even for query samples with newoutfits that do not include faces. Moreover, we claim that existing CC-ReIDbenchmarks do not fully represent real-world scenarios, and propose a new videoCC-ReID dataset called 42Street, based on a theater play that includes crowdedscenes and numerous clothes changes. When applied to multiple ReID models, ourmethod (GEFF) achieves an average improvement of 33.5% and 6.7% in the Top-1clothes-changing metric on the PRCC and LTCC benchmarks. Combined with thelatest ReID models, our method achieves new SOTA results on the PRCC, LTCC,CCVID, LaST and VC-Clothes benchmarks and the proposed 42Street dataset.

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