Robust face recognition based on three dimensional data
type: Dataset
contact point: Organization
  fn: CCSD
keyword: reconnaissance-de-visages-en-2d-3d
keyword: facial-representation
keyword: representation-faciale
keyword: asymetric-face-recognition
keyword: theses
keyword: spiotherengineering-sciences-physicsother
keyword: infoeu-reposemanticsdoctoralthesis
keyword: reconnaissance-multimodale
keyword: reconnaissance-faciale-asymetrique
keyword: 2-d-3-d-multi-modal-face-recognition
landing page: https://theses.hal.science/tel-00693158
description: The face is one of the best biometrics for person identification and verification related applications, because it is natural, non-intrusive, and socially weIl accepted. Unfortunately, an human faces are similar to each other and hence offer low distinctiveness as compared with other biometrics, e.g., fingerprints and irises. Furthermore, when employing facial texture images, intra-class variations due to factors as diverse as illumination and pose changes are usually greater than inter-class ones, making 2D face recognition far from reliable in the real condition. Recently, 3D face data have been extensively investigated by the research community to deal with the unsolved issues in 2D face recognition, Le., illumination and pose changes. This Ph.D thesis is dedicated to robust face recognition based on three dimensional data, including only 3D shape based face recognition, textured 3D face recognition as well as asymmetric 3D-2D face recognition. In only 3D shape-based face recognition, since 3D face data, such as facial pointclouds and facial scans, are theoretically insensitive to lighting variations and generally allow easy pose correction using an ICP-based registration step, the key problem mainly lies in how to represent 3D facial surfaces accurately and achieve matching that is robust to facial expression changes. In this thesis, we design an effective and efficient approach in only 3D shape based face recognition. For facial description, we propose a novel geometric representation based on extended Local Binary Pattern (eLBP) depth maps, and it can comprehensively describe local geometry changes of 3D facial surfaces; while a 81FT -based local matching process further improved by facial component and configuration constraints is proposed to associate keypoints between corresponding facial representations of different facial scans belonging to the same subject. Evaluated on the FRGC v2.0 and Gavab databases, the proposed approach proves its effectiveness. Fu…
identifier: NNT: 2011ECDL0025
issued: 2026-05-20T06:03:41.156624
language: en
modified: 2026-05-20T06:03:41.156634

test_moissonnage_selune
type: Agent

Robust face recognition based on three dimensional data
type: Distribution
access URL: https://theses.hal.science/tel-00693158
format: HTML
issued: 2026-05-20T06:03:41.181026
modified: 2026-05-20T06:03:41.123754