  Cross-modality person re-identification (cm-ReID) is a challenging but key
technology for intelligent video analysis. Existing works mainly focus on
learning common representation by embedding different modalities into a same
feature space. However, only learning the common characteristics means great
information loss, lowering the upper bound of feature distinctiveness. In this
paper, we tackle the above limitation by proposing a novel cross-modality
shared-specific feature transfer algorithm (termed cm-SSFT) to explore the
potential of both the modality-shared information and the modality-specific
characteristics to boost the re-identification performance. We model the
affinities of different modality samples according to the shared features and
then transfer both shared and specific features among and across modalities. We
also propose a complementary feature learning strategy including modality
adaption, project adversarial learning and reconstruction enhancement to learn
discriminative and complementary shared and specific features of each modality,
respectively. The entire cm-SSFT algorithm can be trained in an end-to-end
manner. We conducted comprehensive experiments to validate the superiority of
the overall algorithm and the effectiveness of each component. The proposed
algorithm significantly outperforms state-of-the-arts by 22.5% and 19.3% mAP on
the two mainstream benchmark datasets SYSU-MM01 and RegDB, respectively.
