Title :

3D Face Recognition Under Expressions,Occlusions and Pose Variations

Author(s) :

Drira, Hassen [Auteur]
FOX MIIRE [LIFL]
Ben Amor, Boulbaba [Auteur]
FOX MIIRE [LIFL]
Anuj, Srivastava [Auteur]
Department of Statistics [Tallahassee, FL]
Daoudi, Mohamed [Auteur]
FOX MIIRE [LIFL]
Slama, Rim [Auteur]
FOX MIIRE [LIFL]

Journal title :

IEEE Transactions on Pattern Analysis and Machine Intelligence

Pages :

2270 - 2283

Publisher :

Institute of Electrical and Electronics Engineers

Publication date :

2013-02-21

ISSN :

0162-8828

English keyword(s) :

3D face recognition
shape analysis
biometrics
quality control
data restoration

HAL domain(s) :

Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
Sciences de l'Homme et Société/Psychologie

English abstract : [en]

We propose a novel geometric framework for analyzing 3D faces, with the specific goals of comparing, matching, and averaging their shapes. Here we represent facial surfaces by radial curves emanating from the nose tips and ...
Show more >We propose a novel geometric framework for analyzing 3D faces, with the specific goals of comparing, matching, and averaging their shapes. Here we represent facial surfaces by radial curves emanating from the nose tips and use elastic shape analysis of these curves to develop a Riemannian framework for analyzing shapes of full facial surfaces. This representation, along with the elastic Riemannian metric, seems natural for measuring facial deformations and is robust to challenges such as large facial expressions (especially those with open mouths), large pose variations, missing parts, and partial occlusions due to glasses, hair, etc. This framework is shown to be promising from both - empirical and theoretical - perspectives. In terms of the empirical evaluation, our results match or improve the state-of-the-art methods on three prominent databases: FRGCv2, GavabDB, and Bosphorus, each posing a different type of challenge. From a theoretical perspective, this framework allows for formal statistical inferences, such as the estimation of missing facial parts using PCA on tangent spaces and computing average shapes.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

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

Source :

Harvested from HAL