Gait Recognition Based on 3D Skeleton Data and Graph Convolutional Network

Gait recognition is a hot topic in the field of biometrics because of its unique advantages such as non-contact and long distance. The appearance-based gait recognition methods usually extract features from the silhouettes of human body, which are easy to be affected by factors such as clothing and carrying objects. Although the model-based methods can effectively reduce the influence of appearance factors, it has high computational complexity. Therefore, this paper proposes a gait recognition method based on the 3D skeleton data and graph convolutional network. The 3D skeleton data is robust to the change of view. In this paper, we extract 3D joint feature and 3D bone feature based on 3D skeleton data, design a dual graph convolutional network to extract corresponding gait features and fuse them at feature level. At the same time, we use a multi-loss strategy to combine center loss and softmax loss to optimize the network. Our method is evaluated on the dataset CASIA B. The experimental results show that the proposed method can achieve state-of-the-art performance, and it can effectively reduce the influence of view, clothing and other factors.