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dc.contributor.advisorGao, Yongsheng
dc.contributor.authorChen, Weipingen_US
dc.date.accessioned2018-01-23T02:15:57Z
dc.date.available2018-01-23T02:15:57Z
dc.date.issued2012en_US
dc.identifier.urihttp://hdl.handle.net/10072/365220
dc.description.abstractAutomatically recognizing human faces has attracted a lot of attention in the academic, commercial, and industrial communities during the last few decades due to its law intrusiveness and less cooperativeness. Face recognition technology has a variety of potential applications in information security, law enforcement, surveillance, smart cards, and access control. Despite significant advances in face recognition technology, it has yet to be put to wide use in industrial or commercial communities, mainly because of high error rates in real scenarios. Existing face recognition systems have achieved promising recognition accuracy under controlled condition. However, these systems are highly sensitive to environmental factors due to changing appearance of human face, such as variations in expression, illumination, pose, partial occlusion, and time gap between training and testing data capture. A practical face recognition system should be more robust against these varying conditions. Especially in some applications such as access control to sensitive areas, monitoring border crossing, and identifying criminals or terrorists, the system should be capable of identifying individuals who use disguise accessories to hide one’s identity to remain elusive from low enforcement. Furthermore, many reported face recognition techniques rely heavily on the size and representative of training set, and most of them will suffer serious performance drop or even fail to work if only one training sample per person is available to the system. Hence, face recognition from one sample per person is an important but challenging problem both in theory and for real-world applications. Fewer samples per person mean less laborious effort for collecting them, lower costs for storing and processing them.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.keywordsFace recognition technologyen_US
dc.subject.keywordsStringfaceen_US
dc.subject.keywordsLine edge mapen_US
dc.subject.keywordsString-to-string matchen_US
dc.subject.keywordsFace sketch recognitionen_US
dc.titleFace Recognition using Stringfaceen_US
dc.typeGriffith thesisen_US
gro.facultyScience, Environment, Engineering and Technologyen_US
gro.rights.copyrightThe author owns the copyright in this thesis, unless stated otherwise.
gro.hasfulltextFull Text
dc.contributor.otheradvisorBusch, Andrew
dc.rights.accessRightsPublicen_US
gro.identifier.gurtIDgu1344819854818en_US
gro.source.ADTshelfnoADT0en_US
gro.source.GURTshelfnoGURT1294en_US
gro.thesis.degreelevelThesis (PhD Doctorate)en_US
gro.thesis.degreeprogramDoctor of Philosophy (PhD)en_US
gro.departmentGriffith School of Engineeringen_US
gro.griffith.authorChen, Weiping


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