Titre :

A Novel Geometric Framework on Gram Matrix Trajectories for Human Behavior Understanding

Auteur(s) :

Kacem, Anis [Auteur]
Université de Lille
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Daoudi, Mohamed [Auteur]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Lille Douai]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Ben Amor, Boulbaba [Auteur]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Lille Douai]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Berretti, Stefano [Auteur]
Università degli Studi di Firenze = University of Florence = Université de Florence [UniFI]
Alvarez-Paiva, Juan Carlos [Auteur]
Université de Lille
Laboratoire Paul Painlevé - UMR 8524 [LPP]

Titre de la revue :

IEEE Transactions on Pattern Analysis and Machine Intelligence

Pagination :

1-14

Éditeur :

Institute of Electrical and Electronics Engineers

Date de publication :

2020-01-01

ISSN :

0162-8828

Mot(s)-clé(s) en anglais :

Emotion Recognition from Body Movements
Action Recognition
Facial Expression Recognition !
Gram matrices
Riemannian Geometry
Symmetric Positive Semidefinite Manifolds
Grassmann Manifold

Discipline(s) HAL :

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

Résumé en anglais : [en]

In this paper, we propose a novel space-time geometric representation of human landmark configurations and derive tools for comparison and classification. We model the temporal evolution of landmarks as parametrized ...
Lire la suite >In this paper, we propose a novel space-time geometric representation of human landmark configurations and derive tools for comparison and classification. We model the temporal evolution of landmarks as parametrized trajectories on the Riemannian manifold of positive semidefinite matrices of fixed-rank. Our representation has the benefit to bring naturally a second desirable quantity when comparing shapes-the spatial covariance-in addition to the conventional affine-shape representation. We derived then geometric and computational tools for rate-invariant analysis and adaptive re-sampling of trajectories, grounding on the Riemannian geometry of the underlying manifold. Specifically, our approach involves three steps: (1) landmarks are first mapped into the Riemannian manifold of positive semidefinite matrices of fixed-rank to build time-parameterized trajectories; (2) a temporal warping is performed on the trajectories, providing a geometry-aware (dis-)similarity measure between them; (3) finally, a pairwise proximity function SVM is used to classify them, incorporating the (dis-)similarity measure into the kernel function. We show that such representation and metric achieve competitive results in applications as action recognition and emotion recognition from 3D skeletal data, and facial expression recognition from videos. Experiments have been conducted on several publicly available up-to-date benchmarks.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Projet ANR :

Plateforme Recherche et Innovation dans les Environnements Visuels Numériques et Interactifs

Collections :

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

Source :

Harvested from HAL