Analyzing Trajectories on Grassmann Manifold for Early Emotion Detection from Depth Videos

Alashkar, Taleb; Ben Amor, Boulbaba; Berretti, Stefano; Daoudi, Mohamed

Document type :

Communication dans un congrès avec actes

Title :

Analyzing Trajectories on Grassmann Manifold for Early Emotion Detection from Depth Videos

Author(s) :

Alashkar, Taleb [Auteur]
FOX MIIRE [LIFL]
Ben Amor, Boulbaba [Auteur]
FOX MIIRE [LIFL]
Berretti, Stefano [Auteur]
Dipartimento di Sistemi e Informatica [DSI]
Daoudi, Mohamed [Auteur]
FOX MIIRE [LIFL]

Conference title :

IEEE International Conference on Automatic Face and Gesture Recognition, FG 2015

City :

Ljubljana

Country :

Slovénie

Start date of the conference :

2015-05-05

Journal title :

11th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2015

HAL domain(s) :

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

English abstract : [en]

— This paper proposes a new framework for online detection of spontaneous emotions from low-resolution depth se-quences of the upper part of the body. To face the challenges of this scenario, depth videos are decomposed ...
Show more >— This paper proposes a new framework for online detection of spontaneous emotions from low-resolution depth se-quences of the upper part of the body. To face the challenges of this scenario, depth videos are decomposed into subsequences, each modeled as a linear subspace, which in turn is represented as a point on a Grassmann manifold. Modeling the temporal evolution of distances between subsequences of the underlying manifold as a one-dimensional signature, termed Geometric Motion History, permits us to encompass the temporal signature into an early detection framework using Structured Output SVM, thus enabling online emotion detection. Results obtained on the publicly available Cam3D Kinect database validate the proposed solution, also demonstrating that the upper body, instead of the face alone, can improve the performance of emotion detection.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

Files

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Open access
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Open access
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