Modal-identity dual-central loss for visible-infrared person re-identification

Visible-Infrared person re-identification technology aims to match the target persons across the visible and infrared modalities. In this paper, we propose a visible-infrared person re-identification method based on a modal-identity dual-central loss. Modal-identity dual-central loss constrains the network to extract modal shared features by pulling in the infrared modal center and visible modal center of the same identity person, while pushing away the identity centers of different person to maintain inter-class discriminability. In addition, to extract more discriminative information, we propose a feature pyramid integration network based on efficient channel attention. Specifically, the network fuses high-level features and fine-grained low-level features to build a multi-scale feature map, and introduces an efficient channel attention module to enhance the salient features of person. Extensive experiments have been conducted to validate our proposed method on the SYSU-MM01 and RegDB datasets.