Cross-modality person re-identification technique aims to identify specific pedestrians from pedestrian images captured by cameras with different modalities in non-overlapping fields of view. There are huge cross-modality differences between pedestrian images as well as intra-modality differences, resulting in poor recognition rates. In order to solve this problem, this paper proposed a cross-modality person re-identification method using local supervision(LSN). Firstly, it converted the visible images into grayscale images that were closer to the infrared images to mitigate the cross-modality differences at the image level, and extracted discriminative shared features using a two-stream network with shared parameters to mitigate the cross-modality differences at the feature level. Secondly, it designed a local supervision network to enhance the robustness to background, occlusion and other noises and mitigate the intra-modality differences. Finally, it designed a cross-modality group loss in combination with the identity loss to constrain the network. The experimental results show that the evaluation metrics rank-1 and mAP reach 53.31% and 50.88% on the SYSU-MM01 dataset, and 73.51% and 68.55% on the RegDB dataset, respectively. The experimental results outperform similar methods, which verifies the effectiveness and advancement of the proposed method.

[2022-10-19] Accepted Paper /doc/2022.07.0390/v1

[2023-04-05] Printed Article /doc/2022.07.0390/v2