  The key of visible-infrared person re-identification (VIReID) lies in how to
minimize the modality discrepancy between visible and infrared images. Existing
methods mainly exploit the spatial information while ignoring the
discriminative frequency information. To address this issue, this paper aims to
reduce the modality discrepancy from the frequency domain perspective.
Specifically, we propose a novel Frequency Domain Nuances Mining (FDNM) method
to explore the cross-modality frequency domain information, which mainly
includes an amplitude guided phase (AGP) module and an amplitude nuances mining
(ANM) module. These two modules are mutually beneficial to jointly explore
frequency domain visible-infrared nuances, thereby effectively reducing the
modality discrepancy in the frequency domain. Besides, we propose a
center-guided nuances mining loss to encourage the ANM module to preserve
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. Specifically, our method
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, respectively. Besides,
we also validate the effectiveness and generalization of our method on the
challenging visible-infrared face recognition task. \textcolor{magenta}{The
code will be available.}
