Gender and 3D Facial Symmetry: What's the ...
Type de document :
Communication dans un congrès avec actes
Titre :
Gender and 3D Facial Symmetry: What's the Relationship?
Auteur(s) :
Xia, Baiqiang [Auteur]
FOX MIIRE [LIFL]
Ben Amor, Boulbaba [Auteur]
Institut TELECOM/TELECOM Lille1
FOX MIIRE [LIFL]
Drira, Hassen [Auteur]
FOX MIIRE [LIFL]
Institut TELECOM/TELECOM Lille1
Daoudi, Mohamed [Auteur]
FOX MIIRE [LIFL]
Institut TELECOM/TELECOM Lille1
Ballihi, Lahoucine [Auteur]
FOX MIIRE [LIFL]
FOX MIIRE [LIFL]
Ben Amor, Boulbaba [Auteur]
Institut TELECOM/TELECOM Lille1
FOX MIIRE [LIFL]
Drira, Hassen [Auteur]
FOX MIIRE [LIFL]
Institut TELECOM/TELECOM Lille1
Daoudi, Mohamed [Auteur]
FOX MIIRE [LIFL]
Institut TELECOM/TELECOM Lille1
Ballihi, Lahoucine [Auteur]
FOX MIIRE [LIFL]
Titre de la manifestation scientifique :
10th IEEE Conference on Automatic Face and Gesture Recognition (FG 2013)
Ville :
shanghai
Pays :
Chine
Date de début de la manifestation scientifique :
2013-04-22
Titre de l’ouvrage :
Proceeding of the IEEE Conference on Automatic Face and Gesture Recognition 2013
Date de publication :
2013-04-22
Discipline(s) HAL :
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
Résumé en anglais : [en]
Although it is valuable information that human faces are approximately symmetric, in the literature of facial attributes recognition, little consideration has been given to the relationship between gender, age, ethnicity, ...
Lire la suite >Although it is valuable information that human faces are approximately symmetric, in the literature of facial attributes recognition, little consideration has been given to the relationship between gender, age, ethnicity, etc. and facial asymmetry. In this paper we present a new approach based on bilateral facial asymmetry for gender classification. For that purpose, we propose to first capture the facial asymmetry by using Deformation Scalar Field (DSF) applied on each 3D face, then train such representations (DSFs) with several classifiers, including Random Forest, Adaboost and SVM after PCAbased feature space transformation. Experiments conducted on FRGCv2 dataset showed that a significant relationship exists between gender and facial symmetry when achieving a 90.99% correct classification rate for the 466 earliest scans of subjects (mainly neutral) and 88.12% on the whole FRGCv2 dataset (including facial expressions).Lire moins >
Lire la suite >Although it is valuable information that human faces are approximately symmetric, in the literature of facial attributes recognition, little consideration has been given to the relationship between gender, age, ethnicity, etc. and facial asymmetry. In this paper we present a new approach based on bilateral facial asymmetry for gender classification. For that purpose, we propose to first capture the facial asymmetry by using Deformation Scalar Field (DSF) applied on each 3D face, then train such representations (DSFs) with several classifiers, including Random Forest, Adaboost and SVM after PCAbased feature space transformation. Experiments conducted on FRGCv2 dataset showed that a significant relationship exists between gender and facial symmetry when achieving a 90.99% correct classification rate for the 466 earliest scans of subjects (mainly neutral) and 88.12% on the whole FRGCv2 dataset (including facial expressions).Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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