  Most existing cross-modality person re-identification works rely on
discriminative modality-shared features for reducing cross-modality variations
and intra-modality variations. Despite some initial success, such
modality-shared appearance features cannot capture enough modality-invariant
discriminative information due to a massive discrepancy between RGB and
infrared images. To address this issue, on the top of appearance features, we
further capture the modality-invariant relations among different person parts
(referred to as modality-invariant relation features), which are the complement
to those modality-shared appearance features and help to identify persons with
similar appearances but different body shapes. To this end, a Multi-level
Two-streamed Modality-shared Feature Extraction (MTMFE) sub-network is
designed, where the modality-shared appearance features and modality-invariant
relation features are first extracted in a shared 2D feature space and a shared
3D feature space, respectively. The two features are then fused into the final
modality-shared features such that both cross-modality variations and
intra-modality variations can be reduced. Besides, a novel cross-modality
quadruplet loss is proposed to further reduce the cross-modality variations.
Experimental results on several benchmark datasets demonstrate that our
proposed method exceeds state-of-the-art algorithms by a noticeable margin.
