Frequency Domain Nuances Mining for Visible-Infrared Person Re-Identification

This paper focuses on the visible-infrared person re-identification (VIReID) task, which is essential for information forensics and security as it enables accurate person re-identification across low-light or nighttime conditions. The primary challenge in the VIReID task is to reduce the modality discrepancy between visible and infrared images. Current methods mainly utilize the spatial information, often neglecting the discriminative potential of frequency information. To address this issue, this paper aims to mitigate the modality discrepancy from a frequency domain perspective. Specifically, we propose a novel Frequency Domain Nuances Mining (FDNM) method, which mainly includes a Salience-guided Phase Enhancement (SPE) module and an Amplitude Nuances Mining (ANM) module, to effectively explore the cross-modality frequency domain information. These two modules are mutually beneficial to jointly explore frequency-domain visible-infrared nuances, thereby significantly reducing the modality discrepancy in the frequency domain. Additionally, we propose a Center-guided Nuances Mining (CNM) loss to ensure that the ANM module retains discriminative identity information while discovering diverse cross-modality nuances. Extensive experiments show that the proposed FDNM has significant advantages in improving the performance of VIReID. For instance, our method respectively outperforms the second-best method by 5.2% in Rank-1 accuracy and 5.8% in mAP on the SYSU-MM01 dataset under the indoor search mode. Furthermore, we also demonstrate the effectiveness and generalization of the proposed FDNM method in the challenging visible-infrared face recognition task.