3D face recognition: A robust multi-matcher ...
Document type :
Communication dans un congrès avec actes
Title :
3D face recognition: A robust multi-matcher approach to data degradations
Author(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]
Institut TELECOM/TELECOM Lille1
FOX MIIRE [LIFL]
Drira, Hassen [Auteur]
FOX MIIRE [LIFL]
Mohamed, Daoudi [Auteur]
Institut TELECOM/TELECOM Lille1
FOX MIIRE [LIFL]
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]
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]
Institut TELECOM/TELECOM Lille1
FOX MIIRE [LIFL]
Drira, Hassen [Auteur]

FOX MIIRE [LIFL]
Mohamed, Daoudi [Auteur]
Institut TELECOM/TELECOM Lille1
FOX MIIRE [LIFL]
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]
Conference title :
5th IAPR International Conference on Biometrics, 2012.
City :
New Delhi
Country :
Inde
Start date of the conference :
2012-03-29
Publication date :
2012-03-29
HAL domain(s) :
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
English abstract : [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 ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
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