3D face recognition with asymptotic cones based principal curvatures

Handle URI:
http://hdl.handle.net/10754/577097
Title:
3D face recognition with asymptotic cones based principal curvatures
Authors:
Tang, Yinhang; Sun, Xiang ( 0000-0003-0242-0319 ) ; Huang, Di; Morvan, Jean-Marie; Wang, Yunhong; Chen, Liming
Abstract:
The classical curvatures of smooth surfaces (Gaussian, mean and principal curvatures) have been widely used in 3D face recognition (FR). However, facial surfaces resulting from 3D sensors are discrete meshes. In this paper, we present a general framework and define three principal curvatures on discrete surfaces for the purpose of 3D FR. These principal curvatures are derived from the construction of asymptotic cones associated to any Borel subset of the discrete surface. They describe the local geometry of the underlying mesh. First two of them correspond to the classical principal curvatures in the smooth case. We isolate the third principal curvature that carries out meaningful geometric shape information. The three principal curvatures in different Borel subsets scales give multi-scale local facial surface descriptors. We combine the proposed principal curvatures with the LNP-based facial descriptor and SRC for recognition. The identification and verification experiments demonstrate the practicability and accuracy of the third principal curvature and the fusion of multi-scale Borel subset descriptors on 3D face from FRGC v2.0.
KAUST Department:
Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2015 International Conference on Biometrics (ICB)
Issue Date:
May-2015
DOI:
10.1109/ICB.2015.7139111
Type:
Conference Paper
Appears in Collections:
Conference Papers; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorTang, Yinhangen
dc.contributor.authorSun, Xiangen
dc.contributor.authorHuang, Dien
dc.contributor.authorMorvan, Jean-Marieen
dc.contributor.authorWang, Yunhongen
dc.contributor.authorChen, Limingen
dc.date.accessioned2015-09-10T14:17:56Zen
dc.date.available2015-09-10T14:17:56Zen
dc.date.issued2015-05en
dc.identifier.doi10.1109/ICB.2015.7139111en
dc.identifier.urihttp://hdl.handle.net/10754/577097en
dc.description.abstractThe classical curvatures of smooth surfaces (Gaussian, mean and principal curvatures) have been widely used in 3D face recognition (FR). However, facial surfaces resulting from 3D sensors are discrete meshes. In this paper, we present a general framework and define three principal curvatures on discrete surfaces for the purpose of 3D FR. These principal curvatures are derived from the construction of asymptotic cones associated to any Borel subset of the discrete surface. They describe the local geometry of the underlying mesh. First two of them correspond to the classical principal curvatures in the smooth case. We isolate the third principal curvature that carries out meaningful geometric shape information. The three principal curvatures in different Borel subsets scales give multi-scale local facial surface descriptors. We combine the proposed principal curvatures with the LNP-based facial descriptor and SRC for recognition. The identification and verification experiments demonstrate the practicability and accuracy of the third principal curvature and the fusion of multi-scale Borel subset descriptors on 3D face from FRGC v2.0.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.title3D face recognition with asymptotic cones based principal curvaturesen
dc.typeConference Paperen
dc.contributor.departmentVisual Computing Center (VCC)en
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journal2015 International Conference on Biometrics (ICB)en
dc.contributor.institutionUniversite de Lyon, CNRS, Ecole Centrale de Lyon, LIRIS, Lyon, 69134, Franceen
dc.contributor.institutionIRIP, School of Computer Science and Engineering, Beihang Universtiy, Beijing 100191, Chinaen
dc.contributor.institutionUniversite de Lyon, CNRS, Universite Claude Bernard Lyon 1, ICJ UMR 5208, Villeurbanne F-69622, Franceen
kaust.authorSun, Xiangen
kaust.authorMorvan, Jean-Marieen
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