  Person re-identification aims to identify whether pairs of images belong to
the same person or not. This problem is challenging due to large differences in
camera views, lighting and background. One of the mainstream in learning CNN
features is to design loss functions which reinforce both the class separation
and intra-class compactness. In this paper, we propose a novel Orthogonal
Center Learning method with Subspace Masking for person re-identification. We
make the following contributions: (i) we develop a center learning module to
learn the class centers by simultaneously reducing the intra-class differences
and inter-class correlations by orthogonalization; (ii) we introduce a subspace
masking mechanism to enhance the generalization of the learned class centers;
and (iii) we devise to integrate the average pooling and max pooling in a
regularizing manner that fully exploits their powers. Extensive experiments
show that our proposed method consistently outperforms the state-of-the-art
methods on the large-scale ReID datasets including Market-1501, DukeMTMC-ReID,
CUHK03 and MSMT17.
