SpiderNet: A spiderweb graph neural network for multi-view gait recognition

Abstract

Human gait is a proven biometric trait with applications in security for authentication and disease diagnosis. However, it is one-sided to express and interpret gait data from a single point of view, which cannot reflect multi-dimensional characteristics of gait changes. Moreover, if the gait pattern observed from other views has pathological or abnormal behavior, or has micro movement, it is not easy to be detected and thus affects the recognition rate of gait. In addition, the multi-view fusion of gait knowledge can be challenging due to the close correlation between various visual angles. Owing to the above facts, we propose a spiderweb graph neural network (SpiderNet) to solve the multi-view gait recognition problem, which connects the gait data of single view with that of other views concurrently and constructs an active graph convolutional neural network. The gait trajectory of each view is analyzed by the combination of a memory module and a capsule module, which accomplishes the multi-view feature fusion, as well as the spatio-temporal feature extraction of single view. The experimental results show that the SpiderNet is superior to fifteen state-of-the-art methods, such as random forest, long-short term memory and convolutional neural network, and achieves 98.54%, 98.77%, and 96.91% of the results on three challenging gait datasets: SDUgait, CASIA-B, and OU-MVLP.