Analyzing Trajectories on Grassmann Manifold ...
Type de document :
Communication dans un congrès avec actes
Titre :
Analyzing Trajectories on Grassmann Manifold for Early Emotion Detection from Depth Videos
Auteur(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]
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]
Titre de la manifestation scientifique :
IEEE International Conference on Automatic Face and Gesture Recognition, FG 2015
Ville :
Ljubljana
Pays :
Slovénie
Date de début de la manifestation scientifique :
2015-05-05
Titre de la revue :
11th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2015
Discipline(s) HAL :
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
Résumé en anglais : [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 ...
Lire la suite >— 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.Lire moins >
Lire la suite >— 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.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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