SelfGait: A Spatiotemporal Representation Learning Method for Self-supervised Gait Recognition

TL;DR

SelfGait is a self-supervised learning approach that leverages unlabeled gait data to enhance spatiotemporal feature extraction for gait recognition, outperforming existing methods especially with limited labeled data.

Contribution

The paper introduces SelfGait, a novel self-supervised gait recognition method using unlabeled data to improve feature learning with specific spatiotemporal backbones.

Findings

SelfGait outperforms four state-of-the-art methods on CASIA-B and OU-MVLP datasets.

Utilizes unlabeled data effectively for pre-training gait recognition models.

Employs HPM and MTB backbones for multi-scale spatiotemporal feature extraction.

Abstract

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.