Visible-infrared person re-identification with complementary feature fusion and identity consistency learning

The dual-mode 24/7 monitoring systems continuously obtain visible and infrared images in a real scene. However, differences such as color and texture between these cross-modality images pose challenges for visible-infrared person re-identification (ReID). Currently, the general method is modality-shared feature learning or modal-specific information compensation based on style transfer, but the modality differences often result in the inevitable loss of valuable feature information in the training process. To address this issue, A complementary feature fusion and identity consistency learning (CFF-ICL) method is proposed. On the one hand, the multiple feature fusion mechanism based on cross attention is used to promote the features extracted by the two groups of networks in the same modality image to show a more obvious complementary relationship to improve the comprehensiveness of feature information. On the other hand, the designed collaborative adversarial mechanism between dual discriminators and feature extraction network is designed to remove the modality differences, and then construct the identity consistency between visible and infrared images. Experimental results by testing on SYSU-MM01 and RegDB datasets verify the method’s effectiveness and superiority.