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dc.contributor.advisorYoung, Steve
dc.contributor.authorSamaria, Ferdinando Silvestro
dc.date.accessioned2013-08-29T10:31:27Z
dc.date.available2013-08-29T10:31:27Z
dc.date.issued1995-02-14
dc.identifier.otherPhD.19427
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/244871
dc.description.abstractThis dissertation introduces work on face recognition using a novel technique based on Hidden Markov Models (HMMs). Through the integration of a priori structural knowledge with statistical information, HMMs can be used successfully to encode face features. The results reported are obtained using a database of images of 40 subjects, with 5 training images and 5 test images for each. It is shown how standard one-dimensional HMMs in the shape of top-bottom models can be parameterised, yielding successful recognition rates of up to around 85%. The insights gained from top-bottom models are extended to pseudo two-dimensional HMMs, which offer a better and more flexible model, that describes some of the twodimensional dependencies missed by the standard one-dimensional model. It is shown how pseudo two-dimensional HMMs can be implemented, yielding successful recognition rates of up to around 95%. The performance of the HMMs is compared with the Eigenface approach and various domain and resolution experiments are also carried out. Finally, the performance of the HMM is evaluated in a fully automated system, where database images are cropped automatically.en
dc.language.isoenen
dc.rightsAll Rights Reserveden
dc.rights.urihttps://www.rioxx.net/licenses/all-rights-reserved/en
dc.subjectFace recognitionen
dc.subjectFace segmentationen
dc.subjectautomatic feature extractionen
dc.subjectHidden Markov Modelsen
dc.subjectstochastic modellingen
dc.titleFace recognition using Hidden Markov Modelsen
dc.typeThesisen
dc.type.qualificationlevelDoctoral
dc.type.qualificationnameDoctor of Philosophy (PhD)
dc.publisher.institutionUniversity of Cambridgeen
dc.publisher.departmentDepartment of Engineeringen
dc.publisher.departmentTrinity Collegeen
dc.rights.generalThis work was supported by a Trinity College Internal Graduate Studentship and an Olivetti Research Ltd, CASE award.en
dc.identifier.doi10.17863/CAM.14051


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