Joint Gender, Ethnicity and Age Estimation ...
Type de document :
Article dans une revue scientifique: Article original
Titre :
Joint Gender, Ethnicity and Age Estimation from 3D Faces An Experimental Illustration of their Correlations
Auteur(s) :
Xia, Baiqiang [Auteur]
Modeling and Analysis of Static and Dynamic Shapes [3D-SAM]
Ben Amor, Boulbaba [Auteur]
Modeling and Analysis of Static and Dynamic Shapes [3D-SAM]
Daoudi, Mohamed [Auteur]
Modeling and Analysis of Static and Dynamic Shapes [3D-SAM]
Modeling and Analysis of Static and Dynamic Shapes [3D-SAM]
Ben Amor, Boulbaba [Auteur]
Modeling and Analysis of Static and Dynamic Shapes [3D-SAM]
Daoudi, Mohamed [Auteur]

Modeling and Analysis of Static and Dynamic Shapes [3D-SAM]
Titre de la revue :
Image and Vision Computing
Éditeur :
Elsevier
Date de publication :
2017-08-01
ISSN :
0262-8856
Discipline(s) HAL :
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
Résumé en anglais : [en]
Humans present clear demographic traits which allow their peers to recognize their gender and ethnic groups as well as estimate their age. Abundant literature has investigated the problem of automated gender, ethnicity and ...
Lire la suite >Humans present clear demographic traits which allow their peers to recognize their gender and ethnic groups as well as estimate their age. Abundant literature has investigated the problem of automated gender, ethnicity and age recognition from facial images. However, despite the coexistence of these traits, most of the studies have addressed them separately, very little attention has been given to their correlations. In this work, we address the problem of joint demographic estimation and investigate the correlation through the morphological differences in 3D facial shapes. To this end, a set of facial features are extracted to capture the 3D shape differences among the demographic groups. Then, a correlation-based feature selection is applied to highlight salient features and remove redundancy. These features are later fed to Random Forest for gender and ethnicity classification , and age estimation. Extensive experiments conducted on FRGCv2 dataset, under Expression-Dependent and Expression-Independent settings, demonstrate the effectiveness of the proposed approaches for the three traits, and also show the accuracy improvement when considering their correlations. To the best of our knowledge, this is the first study exploring the correlations of these facial soft-biometric traits using 3D faces. This is also the first work which studies the problem of age estimation from 3D Faces.Lire moins >
Lire la suite >Humans present clear demographic traits which allow their peers to recognize their gender and ethnic groups as well as estimate their age. Abundant literature has investigated the problem of automated gender, ethnicity and age recognition from facial images. However, despite the coexistence of these traits, most of the studies have addressed them separately, very little attention has been given to their correlations. In this work, we address the problem of joint demographic estimation and investigate the correlation through the morphological differences in 3D facial shapes. To this end, a set of facial features are extracted to capture the 3D shape differences among the demographic groups. Then, a correlation-based feature selection is applied to highlight salient features and remove redundancy. These features are later fed to Random Forest for gender and ethnicity classification , and age estimation. Extensive experiments conducted on FRGCv2 dataset, under Expression-Dependent and Expression-Independent settings, demonstrate the effectiveness of the proposed approaches for the three traits, and also show the accuracy improvement when considering their correlations. To the best of our knowledge, this is the first study exploring the correlations of these facial soft-biometric traits using 3D faces. This is also the first work which studies the problem of age estimation from 3D Faces.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
Fichiers
- https://hal.archives-ouvertes.fr/hal-01543482/document
- Accès libre
- Accéder au document
- https://hal.archives-ouvertes.fr/hal-01543482/document
- Accès libre
- Accéder au document
- https://hal.archives-ouvertes.fr/hal-01543482/document
- Accès libre
- Accéder au document
- document
- Accès libre
- Accéder au document
- joint-gender-ethnicity-9.pdf
- Accès libre
- Accéder au document
- document
- Accès libre
- Accéder au document
- joint-gender-ethnicity-9.pdf
- Accès libre
- Accéder au document