Adaptive Illumination-Invariant Synergistic Feature Integration in a Stratified Granular Framework for Visible-Infrared Re-Identification

TL;DR

This paper introduces AMINet, a novel adaptive network for visible-infrared person re-identification that effectively handles modality discrepancies and illumination variations through multi-granularity features and adaptive alignment strategies.

Contribution

The paper presents a new adaptive modality interaction network with multi-granularity feature extraction and innovative fusion and alignment techniques for improved VI-ReID performance.

Findings

Achieves 74.75% Rank-1 accuracy on SYSU-MM01 dataset.

Surpasses baseline accuracy by 7.93%.

Outperforms current state-of-the-art methods by 3.95%.

Abstract

Visible-Infrared Person Re-Identification (VI-ReID) plays a crucial role in applications such as search and rescue, infrastructure protection, and nighttime surveillance. However, it faces significant challenges due to modality discrepancies, varying illumination, and frequent occlusions. To overcome these obstacles, we propose \textbf{AMINet}, an Adaptive Modality Interaction Network. AMINet employs multi-granularity feature extraction to capture comprehensive identity attributes from both full-body and upper-body images, improving robustness against occlusions and background clutter. The model integrates an interactive feature fusion strategy for deep intra-modal and cross-modal alignment, enhancing generalization and effectively bridging the RGB-IR modality gap. Furthermore, AMINet utilizes phase congruency for robust, illumination-invariant feature extraction and incorporates an adaptive multi-scale kernel MMD to align feature distributions across varying scales. Extensive experiments on benchmark datasets demonstrate the effectiveness of our approach, achieving a Rank-1 accuracy of $74.75\%$ on SYSU-MM01, surpassing the baseline by $7.93\%$ and outperforming the current state-of-the-art by $3.95\%$.