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Conference papers

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.
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Submitted on : Sunday, May 20, 2018 - 9:40:27 AM
Last modification on : Wednesday, March 23, 2022 - 3:51:17 PM
Long-term archiving on: : Tuesday, August 21, 2018 - 5:21:43 PM

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Nadia Hosni, Hassen Drira, Faten Chaieb, Boulbaba Ben Amor. 3D Gait Recognition based on Functional PCA on Kendall's Shape Space. International Conference on Pattern Recognition, Aug 2018, Beijing, China. ⟨hal-01765404⟩

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