  Cross-spectral person re-identification, which aims to associate identities
to pedestrians across different spectra, faces a main challenge of the modality
discrepancy. In this paper, we address the problem from both image-level and
feature-level in an end-to-end hybrid learning framework named robust feature
mining network (RFM). In particular, we observe that the reflective intensity
of the same surface in photos shot in different wavelengths could be
transformed using a linear model. Besides, we show the variable linear factor
across the different surfaces is the main culprit which initiates the modality
discrepancy. We integrate such a reflection observation into an image-level
data augmentation by proposing the linear transformation generator (LTG).
Moreover, at the feature level, we introduce a cross-center loss to explore a
more compact intra-class distribution and modality-aware spatial attention to
take advantage of textured regions more efficiently. Experiment results on two
standard cross-spectral person re-identification datasets, i.e., RegDB and
SYSU-MM01, have demonstrated state-of-the-art performance.
