IUSTPersonReID: A Culturally-Aware Benchmark for Person Re-Identification in Modest Attire

A. Sedighi Moghaddam, F. Anvari, MJ. Mirshekari, MA. Fakhari, MR. Mohammadi

Computer Vision Lab of Iran University of Science and Technology - CVLab IUST
Iran University of Science and Technology (IUST)

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📢 News 📢

🎉 Our paper titled "A culturally-aware benchmark for Person Re-Identification in modest attire" has been accepted for publication in the Engineering Applications of Artificial Intelligence (EAAI) journal. You can read and download the paper for free until August 16, 2025 using the following link:

👉 Access the paper here

About the Dataset

The IUST_PersonReID dataset was developed to address limitations in existing person re-identification datasets by including cultural and environmental contexts unique to Islamic countries, especially Iran and Iraq. Unlike common datasets, which don’t reflect the clothing styles common in these regions—such as hijabs and other coverings—the IUST_PersonReID dataset represents this diversity, helping to reduce demographic bias and improve model accuracy. Collected from a variety of real-world settings under different lighting, camera angles, indoor & outdoor, and weather conditions, this dataset provides extensive, overlapping views across multiple cameras. By capturing these unique conditions, IUST_PersonReID offers a valuable resource for developing re-ID models that perform more reliably across diverse environments and populations.

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BibTeX

@article{MOGHADDAM2025111494,
title = {A culturally-aware benchmark for Person Re-Identification in modest attire},
journal = {Engineering Applications of Artificial Intelligence},
volume = {158},
pages = {111494},
year = {2025},
issn = {0952-1976},
doi = {https://doi.org/10.1016/j.engappai.2025.111494},
url = {https://www.sciencedirect.com/science/article/pii/S0952197625014964},
author = {Alireza Sedighi Moghaddam and Fatemeh Anvari and Mohammadjavad Mirshekari Haghighi and Mohammadali Fakhari and Mohammad Reza Mohammadi},
}