Title :

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

Author(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]

Conference title :

International Conference on Pattern Recognition

City :

Beijing

Country :

Chine

Start date of the conference :

2018-08-20

Publication date :

2018-08-20

English keyword(s) :

3D gait recognition
Behavioral biometrics
Functional PCA
Kendall's trajectory
Riemannian geometry

HAL domain(s) :

Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]

English abstract : [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 ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL