DTW-based kernel and rank-level fusion for 3D gait recognition using Kinect

This paper presents a new 3D gait recognition method that utilizes the kinect skeleton data for representing the gait signature. We propose to use two new features, namely joint relative distance (JRD) and joint relative angle (JRA), which are robust against view and pose variations. The relevance of each JRD and JRA sequence in representing human gait is evaluated using a genetic algorithm. We also introduce a dynamic time warping-based kernel that takes a collection of JRD or JRA sequences as parameters and computes a dissimilarity measure between the training and the unknown sample. The proposed kernel can effectively handle variable walking speed without any need of extra pre-processing. In addition, we propose a rank-level fusion of JRD and JRA features that can boost the overall recognition performance greatly. The effectiveness of the proposed method is evaluated using a 3D skeletal gait database captured with a Kinect v2 sensor. In our experiments, rank level fusion of joint relative distance (JRD) and joint relative angle (JRA) achieves promising results, as compared against only JRD and only JRA-based gait recognition.