Gait Recognition Algorithm based on Spatial-temporal Graph Neural Network

Abstract

Gait recognition is an emerging biometric recognition technology. Gait features have the advantages of non-contact, long collection distance and so on. It has received extensive attention from researchers in the field of biometric identification. We propose a novel model-based gait recognition method. Early methods were mainly based on appearance. Appearance-based features usually use gait contour maps as input. The gait contour map is easy to obtain and proved to be effective for recognition tasks. However, its appearance will be affected by the changes of clothing and carrying items. Contrast to the contour-based method is the model-based method. We use the human pose estimation algorithm to obtain 3D key points, use the key points coordinates as graph nodes feature to build a spatial-temporal graph, and use graph neural network to extract features for gait recognition tasks. This method is experimented on the large-scale dataset CSAIA-B dataset. The experimental results show that the proposed method can achieve advanced performance. It is also robust to covariate changes.