  Gait recognition plays a vital role in human identification since gait is a
unique biometric feature that can be perceived at a distance. Although existing
gait recognition methods can learn gait features from gait sequences in
different ways, the performance of gait recognition suffers from insufficient
labeled data, especially in some practical scenarios associated with short gait
sequences or various clothing styles. It is unpractical to label the numerous
gait data. In this work, we propose a self-supervised gait recognition method,
termed SelfGait, which takes advantage of the massive, diverse, unlabeled gait
data as a pre-training process to improve the representation abilities of
spatiotemporal backbones. Specifically, we employ the horizontal pyramid
mapping (HPM) and micro-motion template builder (MTB) as our spatiotemporal
backbones to capture the multi-scale spatiotemporal representations.
Experiments on CASIA-B and OU-MVLP benchmark gait datasets demonstrate the
effectiveness of the proposed SelfGait compared with four state-of-the-art gait
recognition methods. The source code has been released at
https://github.com/EchoItLiu/SelfGait.
