Gait based recognition via fusing information from Euclidean and Riemannian manifolds

Gait is a particular periodical type of human motion with several unique characteristics for every person. In this work we focus on the problem of pose based gait recognition. The contribution of the proposed work is threefold. First we represent every gait sequence according to both the deviation of the poses from an appropriate global model, as well as the intra-sequence pose variability. Secondly, we propose a method which allows us to fuse information from feature representations from both Euclidean and Riemannian spaces by mapping data in a Reproducing Kernel Hilbert Space (RKHS). Classification is then performed using a kernelized version of the SRC algorithm. Third we present a new publicly available dataset for pose based gait recognition captured with Kinect V2. Experimental evaluation reveals state-of-the-art performance in both recognition and verification tasks and a capacity for real-time operation.