SelfGait: A Spatiotemporal Representation Learning Method for Self-supervised Gait Recognition
This work addresses gait recognition challenges in practical scenarios with limited labeled data, such as short sequences or varied clothing, offering a solution for human identification applications.
The paper tackles the problem of insufficient labeled data in gait recognition by proposing SelfGait, a self-supervised method that uses unlabeled data for pre-training, achieving improved performance on CASIA-B and OU-MVLP datasets compared to four state-of-the-art methods.
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.