Dual-Path Image Pair Joint Discrimination for Visible-Infrared Person Re-Identification

Because the imaging spectra of infrared images and visible light images are different, there is a huge modal difference between them. Existing methods use image conversion to solve the problem of modal difference between two images, but these methods usually fail to focus on the complete information of images, which lead to the results of cross modal person re-identification are unstable. To solve this problem, we propose a dual-path image pair joint discriminant model (DPJD), which optimizes the distance within and between classes at the same time, and supervises the network learning to identify feature representations. We generate images with different modalities, and separately compose the same modality image pair and different modality image pair so as to overcome the inconsistent alignment issues. In addition, we also propose a discriminant module based on dual-path (DMDP) to improve the generation quality and discrimination accuracy of image pairs. Experiments demonstrate its effectiveness