  Existing person re-identification has achieved great progress in the visible
domain, capturing all the person images with visible cameras. However, in a
24-hour intelligent surveillance system, the visible cameras may be
noneffective at night. In this situation, thermal cameras are the best
supplemental components, which capture images without depending on visible
light. Therefore, in this paper, we investigate the visible-thermal
cross-modality person re-identification (VT Re-ID) problem. In VT Re-ID, there
are two knotty problems should be well handled, cross-modality discrepancy and
intra-modality variations. To address these two issues, we propose focusing on
enhancing the discriminative feature learning (EDFL) with two extreme simple
means from two core aspects, (1) skip-connection for mid-level features
incorporation to improve the person features with more discriminability and
robustness, and (2) dual-modality triplet loss to guide the training procedures
by simultaneously considering the cross-modality discrepancy and intra-modality
variations. Additionally, the two-stream CNN structure is adopted to learn the
multi-modality sharable person features. The experimental results on two
datasets show that our proposed EDFL approach distinctly outperforms
state-of-the-art methods by large margins, demonstrating the effectiveness of
our EDFL to enhance the discriminative feature learning for VT Re-ID.
