# Illumination-Adaptive Person Re-identification

## TL;DR

This paper introduces IA-ReID, a framework for person re-identification that adapts to illumination changes by disentangling illumination from identity features, improving performance across varying lighting conditions.

## Contribution

The paper proposes an Illumination-Identity Disentanglement network that effectively separates illumination effects from identity features in person re-identification tasks.

## Key findings

- The proposed model outperforms existing methods on simulated datasets with illumination variations.
- The framework demonstrates robustness to real-world illumination changes.
- Constructed large-scale datasets to evaluate illumination adaptation in ReID.

## Abstract

Most person re-identification (ReID) approaches assume that person images are captured under relatively similar illumination conditions. In reality, long-term person retrieval is common, and person images are often captured under different illumination conditions at different times across a day. In this situation, the performances of existing ReID models often degrade dramatically. This paper addresses the ReID problem with illumination variations and names it as {\em Illumination-Adaptive Person Re-identification (IA-ReID)}. We propose an Illumination-Identity Disentanglement (IID) network to dispel different scales of illuminations away while preserving individuals' identity information. To demonstrate the illumination issue and to evaluate our model, we construct two large-scale simulated datasets with a wide range of illumination variations. Experimental results on the simulated datasets and real-world images demonstrate the effectiveness of the proposed framework.