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 visible light images and infrared ones. 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 new visible–infrared person re-identification method, called dual-path image pair joint discriminant model (DPJD), which simultaneously optimizes the distance within and between classes, and supervises the network learning to identify feature representations. We generate images with different modalities for the samples, 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 on two benchmark datasets SYSU-MM01 and RegDB demonstrate its effectiveness. • We propose an image pair generation module, which can separate the modal information and attribute information of the image and realize arbitrary modal conversion. • We design an effective dual-path discrimination module to make both modal features and attribute features be fully used for image recognition. • Experimental results on two commonly used benchmark datasets show that our method is more efficient and works better than the compared methods. [ABSTRACT FROM AUTHOR]