3D Gait Recognition based on Functional PCA on Kendall's Shape Space

Abstract

In this paper we propose a novel gait recognition approach from animated 3D skeletal data. Our approach is based on two disparate ideas from Shape Analysis and Functional Data Analysis (FDA) for a joint geometric-functional analysis. That is, skeletal sequences are viewed as time-parametrized trajectories on the Kendall's shape space when scaling, translation and rotation variations are filtered out from fixed-time 3D skeletons. A Riemannian Functional Principal Component Analysis (RFPCA) is carried out on our manifold-valued trajectories in order to build a new basis of principal functions, termed EigenTrajectories. Thus, each trajectory, could be projected into the eigenbasis which give rise to a compact signature, or EigenScores. The latter is fed to pre-trained ‘One-vs-All’ SVM classifiers for identity recognition and authentication. Based on the geometry of the underlying shape space, tools for re-sampling and synchronizing trajectories are naturally derived to apply the proposed variant of FPCA. We have conducted experiments on a subset of the CMU dataset. Our approach shows promising results compared to the state-of-the-art when a compact and robust signature is considered.