  The RGB-infrared cross-modality person re-identification (ReID) task aims to
recognize the images of the same identity between the visible modality and the
infrared modality. Existing methods mainly use a two-stream architecture to
eliminate the discrepancy between the two modalities in the final common
feature space, which ignore the single space of each modality in the shallow
layers. To solve it, in this paper, we present a novel multi-feature space
joint optimization (MSO) network, which can learn modality-sharable features in
both the single-modality space and the common space. Firstly, based on the
observation that edge information is modality-invariant, we propose an edge
features enhancement module to enhance the modality-sharable features in each
single-modality space. Specifically, we design a perceptual edge features (PEF)
loss after the edge fusion strategy analysis. According to our knowledge, this
is the first work that proposes explicit optimization in the single-modality
feature space on cross-modality ReID task. Moreover, to increase the difference
between cross-modality distance and class distance, we introduce a novel
cross-modality contrastive-center (CMCC) loss into the modality-joint
constraints in the common feature space. The PEF loss and CMCC loss jointly
optimize the model in an end-to-end manner, which markedly improves the
network's performance. Extensive experiments demonstrate that the proposed
model significantly outperforms state-of-the-art methods on both the SYSU-MM01
and RegDB datasets.
