VGG-16 based Gait Recognition using Skeleton Features

Abstract

Gait recognition is a new technology that can identify people with various walking patterns. Gait of a person is the manner in which they walk. A person’s walking stride is distinctive due to their body movements. Due to the popularity of the Kinect, human gait can be recognised to using 3D skeletal information. In this paper, Skeleton Gait Energy(SkeGEI) is image-based approach for gait recognition and VGG-16 with multilayer perceptron is proposed to effectively exploit raw depth information collected by the Kinect sensor. To restore as much gait data as possible, fine-tuned VGG-16 is used to extract space - time deep feature data from SkeGEI. Multilayer perceptron is then used to ascertain the connection between the corresponding subject and the gait features. Softmax is used for classification. Experiments on three different datasets show that our technique outclasses the majority of gait recognition methods.