3D Gait Recognition Based on a CNN-LSTM Network with the Fusion of SkeGEI and DA Features

Abstract

Gait recognition is a promising technology in biometrics in video surveillance applications for its characteristics of non-contact and uniqueness. With the popularization of the Kinect sensor, human gait can be recognized based on the 3D skeletal information. For exploiting raw depth data captured by Kinect device effectively, a novel gait recognition approach based on Skeleton Gait Energy Image (SkeGEI) and Relative Distance and Angle (DA) features fusion is proposed. They are fused in backward to complement each other for gait recognition. In order to maintain as much gait information as possible, a CNN-LSTM network is designed to extract the temporal-spatial deep feature information from SkeGEI and DA features. The experiments evaluated on three datasets show that our approach performs superior to most gait recognition approaches with multi-directional and abnormal patterns.