A Benchmark for Gait Recognition under Occlusion Collected by Multi-Kinect SDAS

TL;DR

This paper introduces a new multimodal gait recognition database under occlusion conditions, and proposes a skeleton-based recognition method that improves accuracy in occluded scenarios.

Contribution

The paper presents the OG RGB+D database with multimodal gait data under occlusion and a novel SkeletonGait method utilizing dual skeleton models and spatio-temporal graph convolutional networks.

Findings

SkeletonGait achieves competitive accuracy on OG RGB+D and CAISA-B databases.

The new database supports research on gait recognition under various occlusion types.

Multi-view and multimodal data improve recognition robustness under occlusion.

Abstract

Human gait is one of important biometric characteristics for human identification at a distance. In practice, occlusion usually occurs and seriously affects accuracy of gait recognition. However, there is no available database to support in-depth research of this problem, and state-of-arts gait recognition methods have not paid enough attention to it, thus this paper focuses on gait recognition under occlusion. We collect a new gait recognition database called OG RGB+D database, which breaks through the limitation of other gait databases and includes multimodal gait data of various occlusions (self-occlusion, active occlusion, and passive occlusion) by our multiple synchronous Azure Kinect DK sensors data acquisition system (multi-Kinect SDAS) that can be also applied in security situations. Because Azure Kinect DK can simultaneously collect multimodal data to support different types of gait recognition algorithms, especially enables us to effectively obtain camera-centric multi-person 3D poses, and multi-view is better to deal with occlusion than single-view. In particular, the OG RGB+D database provides accurate silhouettes and the optimized human 3D joints data (OJ) by fusing data collected by multi-Kinects which are more accurate in human pose representation under occlusion. We also use the OJ data to train an advanced 3D multi-person pose estimation model to improve its accuracy of pose estimation under occlusion for universality. Besides, as human pose is less sensitive to occlusion than human appearance, we propose a novel gait recognition method SkeletonGait based on human dual skeleton model using a framework of siamese spatio-temporal graph convolutional networks (siamese ST-GCN). The evaluation results demonstrate that SkeletonGait has competitive performance compared with state-of-art gait recognition methods on OG RGB+D database and popular CAISA-B database.