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Face recognition using Hidden Markov Models

dc.contributorYoung, Steve
dc.creatorSamaria, Ferdinando Silvestro
dc.date.accessioned2018-11-24T13:12:05Z
dc.date.available2013-08-29T10:31:27Z
dc.date.available2018-11-24T13:12:05Z
dc.date.issued1995-02-14
dc.identifierhttps://www.repository.cam.ac.uk/handle/1810/244871
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/123456789/3087
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.
dc.languageen
dc.publisherUniversity of Cambridge
dc.publisherDepartment of Engineering
dc.publisherTrinity College
dc.rightsThis work was supported by a Trinity College Internal Graduate Studentship and an Olivetti Research Ltd, CASE award.
dc.subjectFace recognition
dc.subjectFace segmentation
dc.subjectautomatic feature extraction
dc.subjectHidden Markov Models
dc.subjectstochastic modelling
dc.titleFace recognition using Hidden Markov Models
dc.typeThesis


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