Titre :

Positive/Negative Emotion Detection from RGB-D upper Body Images

Auteur(s) :

Ballihi, Lahoucine [Auteur]
FOX MIIRE [LIFL]
Lablack, Adel [Auteur]
FOX MIIRE [LIFL]
Ben Amor, Boulbaba [Auteur]
FOX MIIRE [LIFL]
Bilasco, Ioan Marius [Auteur]
Université de Lille, Sciences et Technologies
FOX MIIRE [LIFL]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Daoudi, Mohamed [Auteur]
FOX MIIRE [LIFL]

Titre de la manifestation scientifique :

International Workshop on FFER (Face and Facial Expression Recognition from Real World Videos)-ICPR 2014

Ville :

Stockholm

Pays :

Suède

Date de début de la manifestation scientifique :

2014-08-24

Date de publication :

2014

Mot(s)-clé(s) en anglais :

Emotional state
feature extraction
Grassmann manifold
depth features

Discipline(s) HAL :

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

Résumé en anglais : [en]

The ability to identify users'mental states represents a valu-able asset for improving human-computer interaction. Considering that spontaneous emotions are conveyed mostly through facial expressions and the upper Body ...
Lire la suite >The ability to identify users'mental states represents a valu-able asset for improving human-computer interaction. Considering that spontaneous emotions are conveyed mostly through facial expressions and the upper Body movements, we propose to use these modalities together for the purpose of negative/positive emotion classification. A method that allows the recognition of mental states from videos is pro-posed. Based on a dataset composed with RGB-D movies a set of indic-tors of positive and negative is extracted from 2D (RGB) information. In addition, a geometric framework to model the depth flows and capture human body dynamics from depth data is proposed. Due to temporal changes in pixel and depth intensity which characterize spontaneous emo-tions dataset, the depth features are used to define the relation between changes in upper body movements and the affect. We describe a space of depth and texture information to detect the mood of people using upper body postures and their evolution across time. The experimentation has been performed on Cam3D dataset and has showed promising results.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

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

Source :

Harvested from HAL