Visible–Infrared Person Re-Identification via Global Feature Constraints Led by Local Features

Abstract

Smart security is needed for complex scenarios such as all-weather and multi-scene environments, and visible–infrared person re-identification (VI Re-ID) has become a key technique in this field. VI Re-ID is usually modeled as a pattern recognition issue, which faces the problems of inter-modality and intra-modality discrepancies. To alleviate these problems, we designed the Local Features Leading Global Features Network (LoLeG-Net), a representation learning network. Specifically, for cross-modality discrepancies, we employed a combination of ResNet50 and non-local attention blocks to obtain the modality-shareable features and convert the problem to a single-modality person re-identification (Re-ID) problem. For intra-modality variations, we designed global feature constraints led by local features. In this method, the identity loss and hetero-center loss were employed to alleviate intra-modality variations of local features. Additionally, hard sample mining triplet loss combined with identity loss was used, ensuring the effectiveness of global features. With this method, the final extracted global features were much more robust against the background environment, pose differences, occlusion and other noise. The experiments demonstrate that LoLeG-Net is superior to existing works. The result for SYSU-MM01 was Rank-1/mAP 51.40%/51.41%, and the result for RegDB was Rank-1/mAP 76.58%/73.36%.