Gait Recognition via Semi-supervised Disentangled Representation Learning to Identity and Covariate Features

Existing gait recognition approaches typically focus on learning identity features that are invariant to covariates (e.g., the carrying status, clothing, walking speed, and viewing angle) and seldom involve learning features from the covariate aspect, which may lead to failure modes when variations due to the covariate overwhelm those due to the identity. We therefore propose a method of gait recognition via disentangled representation learning that considers both identity and covariate features. Specifically, we first encode an input gait template to get the disentangled identity and covariate features, and then decode the features to simultaneously reconstruct the input gait template and the canonical version of the same subject with no covariates in a semi-supervised manner to ensure successful disentanglement. We finally feed the disentangled identity features into a contrastive/triplet loss function for a verification/identification task. Moreover, we find that new gait templates can be synthesized by transferring the covariate feature from one subject to another. Experimental results on three publicly available gait data sets demonstrate the effectiveness of the proposed method compared with other state-of-the-art methods.