Bi-modal Face Recognition - How combining ...
Type de document :
Communication dans un congrès avec actes
DOI :
Titre :
Bi-modal Face Recognition - How combining 2D and 3D Clues Can Increase the Precision
Auteur(s) :
Aissaoui, Amel [Auteur]
FOX MIIRE [LIFL]
Martinet, Jean [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Université de Lille, Sciences et Technologies
FOX MIIRE [LIFL]
Martinet, Jean [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Université de Lille, Sciences et Technologies
Titre de la manifestation scientifique :
International Conference on Computer Vision Theory and Applications
Ville :
Berlin
Pays :
Allemagne
Date de début de la manifestation scientifique :
2015-03-11
Titre de l’ouvrage :
International Conference on Computer Vision Theory and Applications
Titre de la revue :
International Conference on Computer Vision Theory and Applications
Date de publication :
2015-03
Mot(s)-clé(s) en anglais :
Face recognition
multimodal
2D
3D
LBP
RGB-depth
multimodal
2D
3D
LBP
RGB-depth
Discipline(s) HAL :
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
Résumé en anglais : [en]
This paper introduces a bi-modal face recognition approach. The objective is to study how combining depth and intensity information can increase face recognition precision. In the proposed approach, local features based ...
Lire la suite >This paper introduces a bi-modal face recognition approach. The objective is to study how combining depth and intensity information can increase face recognition precision. In the proposed approach, local features based on LBP (Local Binary Pattern) and DLBP (Depth Local Binary Pattern) are extracted from intensity and depth images respectively. Our approach combines the results of classifiers trained on extracted intensity and depth cues in order to identify faces. Experiments are performed on three datasets: Texas 3D face dataset, BOSPHORUS 3D face dataset and FRGC 3D face dataset. The obtained results demonstrate the enhanced performance of the proposed method compared to mono-modal (2D or 3D) face recognition. Most processes of the proposed system are performed automatically. It leads to a potential prototype of face recognition using the latest RGB-D sensors, such as Microsoft Kinect or Intel RealSense 3D Camera.Lire moins >
Lire la suite >This paper introduces a bi-modal face recognition approach. The objective is to study how combining depth and intensity information can increase face recognition precision. In the proposed approach, local features based on LBP (Local Binary Pattern) and DLBP (Depth Local Binary Pattern) are extracted from intensity and depth images respectively. Our approach combines the results of classifiers trained on extracted intensity and depth cues in order to identify faces. Experiments are performed on three datasets: Texas 3D face dataset, BOSPHORUS 3D face dataset and FRGC 3D face dataset. The obtained results demonstrate the enhanced performance of the proposed method compared to mono-modal (2D or 3D) face recognition. Most processes of the proposed system are performed automatically. It leads to a potential prototype of face recognition using the latest RGB-D sensors, such as Microsoft Kinect or Intel RealSense 3D Camera.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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