Dual adaptive alignment and partitioning network for visible and infrared cross-modality person re-identification

Visible and infrared person re-identification (VI-ReID) describes the task of matching the images of a person, captured by visible-light and infrared cameras; this is a particular challenge in night time surveillance applications. Existing cross-modality recognition studies have been conducted mainly with a focus on learning the global and shareable feature representation of pedestrians to handle cross-modality discrepancies. However, the global features of pedestrian images cannot solve the unaligned image pairs efficiently, particularly when encountering the human appearance or posture misalignment caused by inaccurate pedestrian detection boxes. To mitigate the impact of these problems, we propose an end-to-end dual alignment and partitioning network to simultaneously learn global and local modal invariant features of pedestrians. First, we use two adaptive spatial transform modules to align the visible and infrared input images. Subsequently, the aligned image is divided horizontally, and the features of each local block are extracted. Then, we fuse these local features with global features. To alleviate the differences between heterogeneous modals and learn the common feature representation of heterogeneous modals, we map the features of heterogeneous modes into the same feature embedding space. Finally, we use the combination of identity loss and weighted regularized TriHard loss to improve the recognition accuracy. Extensive experimental results on two cross-modality datasets, RegDB and SYSU-MM01, demonstrate the superiority of the proposed method over other existing state-of-the-art methods.