Integral Pose Learning via Appearance Transfer for Gait Recognition

Panjian Huang, Saihui Hou, Chunshui Cao, Xu Liu, Xuecai Hu, Yongzhen Huang

Abstract:

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 limitedcovariate conditions, downstream models enable to extract discriminative integral 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.