Fusion of Static and Dynamic Parameters at Decision Level in Human Gait Recognition

Abstract

This paper presents the bimodal biometric system based on human gait data of different type: dynamic - ground reaction forces and static - some anthropometric data of human body derived by means of Kinect. The innovation of this work is the use of unprecedented hitherto in the literature set of signals. The study was conducted on a group of 31 people (606 gait cycles). Kistlers force plates and Kinect device as well as the authors software were used to measure and process data. The following anthropometric parameters were used here: torso, hip width, length of left thigh, length of right thigh and body height. These signals have been combined at decision level of the biometric system. Our biometric system in gait recognition process involves both k-nearest neighbour classifier as well as majority voting system. In case of users the False Rejected Rate (FRR) reaches the level of 4.55 % and False Accepted Rate (FAR) is equal to 0.85 %. In the case of impostors it has been possible to reject 26 cases previously classified by 5NN. The presented biometric system fills the gaps in the existing studies and confirms the superiority of systems based fusion over typical methods of human gait recognition.