Title :

A Novel Space-Time Representation on the Positive Semidefinite Cone for Facial Expression Recognition

Author(s) :

Kacem, Anis [Auteur]
Modeling and Analysis of Static and Dynamic Shapes [3D-SAM]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Daoudi, Mohamed [Auteur]
Modeling and Analysis of Static and Dynamic Shapes [3D-SAM]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Nord Europe]
Amor, Boulbaba [Auteur]
Modeling and Analysis of Static and Dynamic Shapes [3D-SAM]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Carlos Alvarez-Paiva, Juan [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]

Conference title :

International Conference on Computer Vision

City :

Venice

Country :

Italie

Start date of the conference :

2017-10-22

Publication date :

2017-10-22

HAL domain(s) :

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

English abstract : [en]

In this paper, we study the problem of facial expression recognition using a novel space-time geometric representation. We describe the temporal evolution of facial landmarks as parametrized trajectories on the Riemannian ...
Show more >In this paper, we study the problem of facial expression recognition using a novel space-time geometric representation. We describe the temporal evolution of facial landmarks as parametrized trajectories on the Riemannian man-ifold of positive semidefinite matrices of fixed-rank. Our representation has the advantage to bring naturally a second desirable quantity when comparing shapes – the spatial covariance – in addition to the conventional affine-shape representation. We derive then geometric and computational tools for rate-invariant analysis and adaptive re-sampling of trajectories, grounding on the Riemannian geometry of the manifold. Specifically, our approach involves three steps: 1) facial landmarks are first mapped into the Riemannian manifold of positive semidefinite matrices of rank 2, to build time-parameterized trajectories; 2) a temporal alignment is performed on the trajectories, providing a geometry-aware (dis-)similarity measure between them; 3) finally, pairwise proximity function SVM (ppfSVM) is used to classify them, incorporating the latter (dis-)similarity measure into the kernel function. We show the effectiveness of the proposed approach on four publicly available benchmarks (CK+, MMI, Oulu-CASIA, and AFEW). The results of the proposed approach are comparable to or better than the state-of-the-art methods when involving only facial landmarks.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

ANR Project :

Plateforme Recherche et Innovation dans les Environnements Visuels Numériques et Interactifs

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL