Learning Bias-Free Representation for Large-Scale Person Re-Identification

Person re-identification (re-ID) aims to match the same person across disjointed cameras. In practice, misalignment is one of the key problems that limits the re-ID accuracy. There are many causes, such as detection errors from automatic detectors, human pose changes and relative movement between people and cameras. However, most current automatic re-ID solutions are still limited in mitigating such misalignments. In this paper, we propose that the misalignment scenarios in re-ID can be divided into three basic types. To further study their effects, we visualize their negative impacts in the learned embedding space and compare the changes in the re-ID accuracy. To address these specific misalignment problems, we design three subnetworks to correct the corresponding misalignments and discuss the performance gains. Moreover, by integrating all subnetworks into one unified structure, we propose a bias-free representation learning method. Unlike previous approaches that mainly focus on one specific kind of misalignment, our proposed method can eliminate the feature bias that is introduced by multiple misalignment problems. Systematic visualizations and comparisons are conducted to demonstrate that our method can efficiently correct the feature bias during the representation learning step. Evaluations on three large-scale datasets show that our proposed bias-free representation learning method can outperform the state-of-the-art methods.