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

Ben Soltana Wael 1 Mohsen Ardabilian 1 Pierre Lemaire 1 Di Huang 1 Szeptycki Przemyslaw 1 Liming Chen 1 Boulbaba Ben Amor 2, 3 Drira Hassen 2 Daoudi Mohamed 2, 3 Nesli Erdogmus 4 Lionel Daniel 4 Jean-Luc Dugelay 4 Joseph Colineau 5
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
2 LIFL - FOX MIIRE
LIFL - Laboratoire d'Informatique Fondamentale de Lille
3 Institut TELECOM/TELECOM Lille1
4 Eurecom [Sophia Antipolis]
5 Thales Research and Technology [Palaiseau]
Abstract : 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.
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Communication dans un congrès
5th IAPR International Conference on Biometrics, 2012., Mar 2012, New Delhi, India. pp.103 - 110, 2012
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Dernière modification le : jeudi 21 février 2019 - 10:52:51
Document(s) archivé(s) le : vendredi 30 novembre 2012 - 03:35:09

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INSTITUT-TELECOM | EC-LYON | LIRIS | EURECOM | CRISTAL | CRISTAL-3D-SAM | INRIA | UNIV-LILLE3 | INSA-LYON | UNIV-LYON1 | IMT-LILLE-DOUAI | INSA-GROUPE | LABEXIMU

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Ben Soltana Wael, Mohsen Ardabilian, Pierre Lemaire, Di Huang, Szeptycki Przemyslaw, et al.. 3D face recognition: A robust multi-matcher approach to data degradations. 5th IAPR International Conference on Biometrics, 2012., Mar 2012, New Delhi, India. pp.103 - 110, 2012. 〈hal-00726254〉

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