A Culturally-Aware Benchmark for Person Re-Identification in Modest Attire
This addresses fairness and bias in ReID research globally by providing a culturally-aware benchmark, though it is incremental as it focuses on dataset creation rather than new methods.
The paper tackles the problem of person re-identification (ReID) models struggling to generalize across diverse cultural contexts, particularly in Islamic regions with modest attire, by introducing the IUST_PersonReId dataset. Experiments show significant performance drops, such as SOLIDER dropping 50.75% mAP compared to Market1501, highlighting the challenges and the dataset's potential for advancing culturally sensitive ReID systems.
Person Re-Identification (ReID) is a fundamental task in computer vision with critical applications in surveillance and security. Despite progress in recent years, most existing ReID models often struggle to generalize across diverse cultural contexts, particularly in Islamic regions like Iran, where modest clothing styles are prevalent. Existing datasets predominantly feature Western and East Asian fashion, limiting their applicability in these settings. To address this gap, we introduce Iran University of Science and Technology Person Re-Identification (IUST_PersonReId), a dataset designed to reflect the unique challenges of ReID in new cultural environments, emphasizing modest attire and diverse scenarios from Iran, including markets, campuses, and mosques. Experiments on IUST_PersonReId with state-of-the-art models, such as Semantic Controllable Self-supervised Learning (SOLIDER) and Contrastive Language-Image Pretraining Re-Identification (CLIP-ReID), reveal significant performance drops compared to benchmarks like Market1501 and Multi-Scene MultiTime (MSMT17), specifically, SOLIDER shows a drop of 50.75% and 23.01% Mean Average Precision (mAP) compared to Market1501 and MSMT17 respectively, while CLIP-ReID exhibits a drop of 38.09% and 21.74% mAP, highlighting the challenges posed by occlusion and limited distinctive features. Sequence-based evaluations show improvements by leveraging temporal context, emphasizing the dataset's potential for advancing culturally sensitive and robust ReID systems. IUST_PersonReId offers a critical resource for addressing fairness and bias in ReID research globally.