Spectral Enhancement and Pseudo-Anchor Guidance for Infrared-Visible Person Re-Identification

TL;DR

This paper introduces SEPG-Net, a novel deep learning model for infrared-visible person re-identification that enhances spectral information and uses pseudo-anchor guidance to improve matching accuracy across modalities.

Contribution

The paper proposes a spectral enhancement scheme based on frequency and greyscale information, and a pseudo-anchor-guided loss to better align infrared and visible features, advancing VI-ReID performance.

Findings

SEPG-Net outperforms existing methods on benchmark datasets.

Spectral enhancement reduces information loss during modality transformation.

Pseudo-anchor guidance improves discriminative feature learning.

Abstract

The development of deep learning has facilitated the application of person re-identification (ReID) technology in intelligent security. Visible-infrared person re-identification (VI-ReID) aims to match pedestrians across infrared and visible modality images enabling 24-hour surveillance. Current studies relying on unsupervised modality transformations as well as inefficient embedding constraints to bridge the spectral differences between infrared and visible images, however, limit their potential performance. To tackle the limitations of the above approaches, this paper introduces a simple yet effective Spectral Enhancement and Pseudo-anchor Guidance Network, named SEPG-Net. Specifically, we propose a more homogeneous spectral enhancement scheme based on frequency domain information and greyscale space, which avoids the information loss typically caused by inefficient modality transformations. Further, a Pseudo Anchor-guided Bidirectional Aggregation (PABA) loss is introduced to bridge local modality discrepancies while better preserving discriminative identity embeddings. Experimental results on two public benchmark datasets demonstrate the superior performance of SEPG-Net against other state-of-the-art methods. The code is available at https://github.com/1024AILab/ReID-SEPG.