3D Gait Recognition based on Functional ...
Type de document :
Communication dans un congrès avec actes
Titre :
3D Gait Recognition based on Functional PCA on Kendall's Shape Space
Auteur(s) :
Hosni, Nadia [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Drira, Hassen [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Chaieb, Faten [Auteur]
Institut National des Sciences Appliquées et de Technologie [Tunis] [INSAT]
Ben Amor, Boulbaba [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Drira, Hassen [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Chaieb, Faten [Auteur]
Institut National des Sciences Appliquées et de Technologie [Tunis] [INSAT]
Ben Amor, Boulbaba [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Titre de la manifestation scientifique :
International Conference on Pattern Recognition
Ville :
Beijing
Pays :
Chine
Date de début de la manifestation scientifique :
2018-08-20
Date de publication :
2018-08-20
Mot(s)-clé(s) en anglais :
3D gait recognition
Behavioral biometrics
Functional PCA
Kendall's trajectory
Riemannian geometry
Behavioral biometrics
Functional PCA
Kendall's trajectory
Riemannian geometry
Discipline(s) HAL :
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
Résumé en anglais : [en]
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 ...
Lire la suite >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.Lire moins >
Lire la suite >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.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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