Dual-modality hard mining triplet-center loss for visible infrared person re-identification

Abstract

Visible infrared person re-identification (VI-reid) has gradually increased in popularity as an crucial branch of person re-identification (reid). It not only has intra-class variations caused by different viewpoint, pedestrian posture changes, complex backgrounds, resolution, occlusions existing in traditional visible visible person re-identification (VV-reid), but also has been subjected to enormous cross-modality discrepancies resulted from the difference in the reflection spectrum of visible light and infrared cameras. In this paper, we put forward a novel loss function, called dual-modality hard mining triplet-center loss (DTCL), to optimize intra-class and inter-class distance and to supervise the network to learn discriminative feature representations. The DTCL learns a visible modality center and an infrared modality center separately for each class and selects online novel cross-modality triplets and intra-modality triplets for each sample respectively. In particular, it makes the samples from the same class closer to the class center and pushes them away from the centers of the different classes. Besides, we also propose a dual-path part-based feature learning network (DPFLN) framework, to learn the local features and solve the problem of cross-modality discrepancies. We conduct experiments on two cross-modality reid datasets, and obtain promising results.