Modality adversarial neural network for visible-thermal person re-identification.

•We propose a modality adversarial learning strategy, making our model aligns the feature representations across different modalities effectively. The adversarial learning strategy can competently eliminate modality discrepancy, which enhances the feature representations for cross-modality identification problem.•Our proposed dual-constrained triplet loss can address the intra- and cross- modality variations effectually. We integrate the dual-constrained triplet loss and the identity loss to enhance the discriminative property of feature representations and to generate the modality-invariant features with adversarial training strategy.•We design a one-stream framework for visible-thermal person re-identification to utilize our adversarial learning strategy and dual-constrained triplet loss. Compared with two-stream networks, the one-stream framework is more stable for adversarial learning. Our extension experiments and analysis abundantly prove that the proposed method outperforms most of the state-of-the-art algorithms for visible-thermal person re-identification.