3D face recognition: A robust multi-matcher approach to data degradations
| Autor: | Lionel Daniel, Liming Chen, Boulbaba Ben Amor, Pierre Lemaire, Hassen Drira, Mohamed Daoudi, Mohsen Ardabilian, Wael Ben Soltana, Nesli Erdogmus, Przemyslaw Szeptycki, Jean-Luc Dugelay, Joseph Colineau, Di Huang |
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| Přispěvatelé: | Extraction de Caractéristiques et Identification (imagine), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), FOX MIIRE (LIFL), Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS), Institut TELECOM/TELECOM Lille1, Institut Mines-Télécom [Paris] (IMT), Eurecom [Sophia Antipolis], Thales Research and Technology [Palaiseau], THALES, Ben Amor, Boulbaba, Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), THALES [France] |
| Jazyk: | angličtina |
| Rok vydání: | 2012 |
| Předmět: |
Image fusion
Decimation business.industry Computer science [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] 020207 software engineering 02 engineering and technology Solid modeling Machine learning computer.software_genre Facial recognition system symbols.namesake [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] Gaussian noise Face model Robustness (computer science) 0202 electrical engineering electronic engineering information engineering symbols 020201 artificial intelligence & image processing Artificial intelligence business Gaussian process computer |
| Zdroj: | 5th IAPR International Conference on Biometrics, 2012. 5th IAPR International Conference on Biometrics, 2012., Mar 2012, New Delhi, India. pp.103-110 ICB |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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