Show simple item record

dc.contributor.advisorGao, Yongsheng
dc.contributor.authorZhang, Xiaozhengen_US
dc.date.accessioned2018-01-23T02:29:14Z
dc.date.available2018-01-23T02:29:14Z
dc.date.issued2008en_US
dc.identifier.urihttp://hdl.handle.net/10072/366373
dc.description.abstractPose invariance is a key ability for face recognition to achieve its advantages of being non-intrusive over other biometric techniques requiring cooperative subjects such as fingerprint recognition and iris recognition. Due to the complex 3D structures and various surface reflectivities of human faces, however, pose variations bring serious challenges to current face recognition systems. The image variations of human faces under 3D transformations are larger than that existing face recognition can tolerate. This research attempts to achieve pose-invariant face recognition through 3D reconstructions, which inversely estimates 3D shape and texture information of human faces from 2D face images. This extracted information is intrinsic features useful for face recognition which is invariable to pose changes. The proposed framework reconstructs personalised 3D face models from images of known people in a database (or gallery views) and generates virtual views in possible poses for face recognition algorithms to match the captured image (or probe view). In particular, three different scenarios of gallery views have been scrutinised: 1) when multiple face images from a fixed viewpoint under different illumination conditions are used as gallery views; 2) when a police mug shot consisting of a frontal view and a side view per person is available as gallery views; and 3) when a single frontal face image per person is used as gallery view. These three scenarios provide the system different amount of information and cover a wide range of situations which a face recognition system will encounter. Three novel 3D reconstruction approaches have then been proposed according to these three scenarios, which are 1) Heterogeneous Specular and Diffuse (HSD) face modelling, 2) Multilevel Quadratic Variation Minimisation (MQVM), and 3) Automatic Facial Texture Synthesis (AFTS), respectively. Experimental results show that these three proposed approaches can effectively improve the performance of face recognition across pose...en_US
dc.languageEnglishen_US
dc.publisherGriffith Universityen_US
dc.publisher.placeBrisbaneen_US
dc.rights.copyrightThe author owns the copyright in this thesis, unless stated otherwise.en_US
dc.subject.keywordspose-invariant face recognitionen_US
dc.subject.keywordspose invarianceen_US
dc.subject.keywordsface recognitionen_US
dc.subject.keywordsface recognition systemsen_US
dc.subject.keywordsface recognition algorithmsen_US
dc.titlePose-invariant Face Recognition through 3D Reconstructionsen_US
dc.typeGriffith thesisen_US
gro.facultyScience, Environment, Engineering and Technologyen_US
gro.hasfulltextFull Text
dc.contributor.otheradvisorDimitrijev, Sima
dc.rights.accessRightsPublicen_US
gro.identifier.gurtIDgu1316995907870en_US
gro.identifier.ADTnumberadt-QGU20100730.111318en_US
gro.source.ADTshelfnoADT0786en_US
gro.thesis.degreelevelThesis (PhD Doctorate)en_US
gro.thesis.degreeprogramDoctor of Philosophy (PhD)en_US
gro.departmentSchool of Engineeringen_US


Files in this item

This item appears in the following Collection(s)

Show simple item record