GENGait: Exploring a Skeleton-Based Gait Recognition Framework With Strong Generalizability

Gait recognition is an emerging remote biometric technology that has applications in various fields such as video surveillance. Silhouette-based approach and skeleton-based approach are the two most popular approaches. And researchers are starting to work on the gait recognition methods with the fusion of the two modalities. Therefore, this article focuses on a skeleton-based gait recognition framework (GENGait) with strong generalization capabilities, laying the foundation for future multimodal fusion. Through previous work we have found that previous studies of skeleton-based methods have devised many skeleton preprocessing methods and used many different backbone networks, but their backbones are usually shallow. However, with the publication of the large gait recognition in the wild (GREW) wild dataset, a deep backbone is critical to the generalisation ability of skeleton-based methods. Moreover, by visualizing and analyzing gait sequences, we found that many methods do not pay attention to the fact that the camera drops frames when taking video, which leads to the introduction of noise in the feature extraction. In view of these problems, this article constructs a generalized skeleton-based deep gait learning framework with strong generalization ability from three aspects: the depth of the backbone network, the preprocessing of skeleton sequences, and the processing of time sequences. The network is trained on the GREW dataset and tested on other datasets to evaluate the generalization ability of the network. Our proposed GENGait achieves better results compared to many state-of-the-art skeleton-based methods.