Combining Face Averageness and Symmetry for 3D-based Gender Classification

Xia, Baiqiang; Ben Amor, Boulbaba; Drira, Hassen; Daoudi, Mohamed; Ballihi, Lahoucine

Type de document :

Article dans une revue scientifique: Article original

Titre :

Combining Face Averageness and Symmetry for 3D-based Gender Classification

Auteur(s) :

Xia, Baiqiang [Auteur]
FOX MIIRE [LIFL]
Ben Amor, Boulbaba [Auteur]
Institut TELECOM/TELECOM Lille1
FOX MIIRE [LIFL]
Drira, Hassen [Auteur]

Institut TELECOM/TELECOM Lille1
FOX MIIRE [LIFL]
Daoudi, Mohamed [Auteur]

FOX MIIRE [LIFL]
Ballihi, Lahoucine [Auteur]
Institut TELECOM/TELECOM Lille1
FOX MIIRE [LIFL]

Titre de la revue :

Pattern Recognition

Pagination :

746-758

Éditeur :

Elsevier

Date de publication :

2015-03-08

ISSN :

0031-3203

Discipline(s) HAL :

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

Résumé en anglais : [en]

Although human face averageness and symmetry are valuable clues in social perception (such as attractiveness, masculinity/femininity, healthy/sick, etc.), in the literature of facial attribute recognition, little consideration ...
Lire la suite >Although human face averageness and symmetry are valuable clues in social perception (such as attractiveness, masculinity/femininity, healthy/sick, etc.), in the literature of facial attribute recognition, little consideration has been given to them. In this work, we propose to study the morphological differences between male and female faces by analyzing the averageness and symmetry of their 3D shapes. In particular, we address the following questions: (i) is there any relationship between gender and face averageness/symmetry? and (ii) if this relationship exists, which specific areas on the face are involved? To this end, we propose first to capture densely both the face shape averageness (AVE) and symmetry (SYM) using our Dense Scalar Field (DSF), which denotes the shooting directions of geodesics between facial shapes. Then, we explore such representations by using classical machine learning techniques, the Feature Selection (FS) methods and Random Forest (RF) classification algorithm. Experiments conducted on the FRGCv2 dataset show a significant relationship exists between gender and facial averageness/symmetry when achieving a classification rate of 93.7% on the 466 earliest scans of subjects (mainly neutral) and 92.4% on the whole FRGCv2 dataset (including facial expressions)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

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