Titre :

3D face recognition: A robust multi-matcher approach to data degradations

Auteur(s) :

Wael, Ben Soltana [Auteur]
Extraction de Caractéristiques et Identification [imagine]
Ardabilian, Mohsen [Auteur]
Extraction de Caractéristiques et Identification [imagine]
Lemaire, Pierre [Auteur]
Extraction de Caractéristiques et Identification [imagine]
Huang, Di [Auteur]
Extraction de Caractéristiques et Identification [imagine]
Przemyslaw, Szeptycki [Auteur]
Extraction de Caractéristiques et Identification [imagine]
Chen, Liming [Auteur]
Extraction de Caractéristiques et Identification [imagine]
Ben Amor, Boulbaba [Auteur]
FOX MIIRE [LIFL]
Institut TELECOM/TELECOM Lille1
Drira, Hassen [Auteur]
FOX MIIRE [LIFL]
Mohamed, Daoudi [Auteur]
FOX MIIRE [LIFL]
Institut TELECOM/TELECOM Lille1
Erdogmus, Nesli [Auteur]
Eurecom [Sophia Antipolis]
Daniel, Lionel [Auteur]
Eurecom [Sophia Antipolis]
Dugelay, Jean-Luc [Auteur]
Eurecom [Sophia Antipolis]
Colineau, Joseph [Auteur]
Thales Research and Technology [Palaiseau]

Titre de la manifestation scientifique :

5th IAPR International Conference on Biometrics, 2012.

Ville :

New Delhi

Pays :

Inde

Date de début de la manifestation scientifique :

2012-03-29

Date de publication :

2012-03-29

Discipline(s) HAL :

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

Résumé en anglais : [en]

Over the past decades, 3D face has emerged as a solution to face recognition due to its reputed invariance to lighting conditions and pose. While proposed approaches have proven their efficiency over renowned databases as ...
Lire la suite >Over the past decades, 3D face has emerged as a solution to face recognition due to its reputed invariance to lighting conditions and pose. While proposed approaches have proven their efficiency over renowned databases as FRGC, less effort was spent on studying the robustness of algorithms to quality degradations. In this paper, we present a study of the robustness of four state of the art algorithms and a multi-matcher framework to face model degradations such as Gaussian noise, decimation, and holes. The four state of the art algorithms were chosen for their different and complementary properties and exemplify the major classes of 3D face recognition solutions. As they displayed different behavior under data degradations, we further designed a fusion framework to best take into account their complementary properties. The proposed multi-matcher scheme is based on an offline and an online weight learning process. Experiments were conducted on a subset of the FRGC database, on which we generated degradations. Results demonstrate the competitive robustness of the proposed approach.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