Integral Pose Learning via Appearance Transfer for Gait Recognition
Panjian Huang, Saihui Hou, Chunshui Cao, Xu Liu, Xuecai Hu, Yongzhen Huang
Abstract
Gait recognition plays an important role in video surveillance and security by identifying humans based on their unique walking patterns. The existing gait recognition methods have achieved competitive accuracy with shape and motion patterns under limited-covariate conditions. However, when extreme appearance changes distort discriminative features, gait recognition yields unsatisfactory results under cross-covariate conditions. In this work, we first indicate that the integral pose in each silhouette maintains an appearance-unrelated discriminative identity. However, the monotonous appearance variables in a gait database cause gait models to have difficulty extracting integral poses. Therefore, we propose an Appearance-transferable Disentangling and Generative Network (GaitApp) to generate gait silhouettes with rich appearances and invariant poses. Specifically, GaitApp leverages multi-branch cooperation to disentangle pose features and appearance features, and transfers the appearance information from one subject to another. By simulating a person constantly changing appearances under limited-covariate conditions, downstream models enable to extract integral discriminative pose features. Extensive experiments demonstrate that our method allows representative gait models to stand at a new altitude, further promoting the exploration to cross-covariate gait recognition. All the code is available at https://github.com/Hpjhpjhs/GaitApp.git.