DHML: Deep Heterogeneous Metric Learning for VIS-NIR Person Re-identification

Narrowing the modal gap in person re-identification between visible domain and near infrared domain (VIS-NIR Re-ID) is a challenging problem. In this paper, we propose the deep heterogeneous metric learning (DHML) for VIS-NIR Re-ID. Our method explicitly learns a specific projection transformation for each modality. Furthermore, we design a heterogeneous metric module (HeMM), and embed it in the deep neural network to complete an end-to-end training. HeMM provides supervisory information to the network, essentially eliminating the cross-modal gap in the feature extraction stage, rather than performing a post-transformation on the extracted features. We conduct a number of experiments on the SYSU-MM01 dataset, the largest existing VIS-NIR Re-ID dataset. Our method achieves state-of-the-art performance and outperforms existing approaches by a large margin.