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dc.contributor.authorYu, Meng
dc.coverage.spatialxvii, 128en_US
dc.date.accessioned2011-12-21T13:52:56Z
dc.date.available2011-12-21T13:52:56Z
dc.date.issued2010
dc.identifieruk.bl.ethos.552453
dc.identifier.urihttps://hdl.handle.net/10023/2124
dc.description.abstractThis thesis establishes a framework for facial feature detection and human face movement tracking. Statistical models of shape and appearance are built to represent the human face structure and interpret target images of human faces. The approach is a patch-based method derived from an earlier proposed method, the constrained local model (CLM) [1] algorithm. In order to increase the ability to track face movements with large head rotations, a 3D shape model is used in the system. And multiple texture models from different viewpoints are used to model the appearance. During fitting or tracking, the current estimate of pose (shape coordinates) is used to select the appropriate texture model. The algorithm uses the shape model and a texture model to generate a set of region template detectors. A search is then performed in the global pose / shape space using these detectors. Different optimisation frameworks are used in the implementation. The training images are created by rendering expressive 3D face models with different scales, rotations, expressions, brightness, etc. Experimental results are demonstrated by fitting the model to image sequences with large head rotations to evaluate the performance of the algorithm. To evaluate the stability and selection of factors of the algorithm, more experiments are carried out. The results show that the proposed 3D constrained local model algorithm improves the performance of the original CLM algorithm for videos with large out-of-plane head rotations.en_US
dc.language.isoenen_US
dc.publisherUniversity of St Andrews
dc.subject.lccTA1650.Y8
dc.subject.lcshHuman face recognition (Computer Science)en_US
dc.subject.lcshFace--Movements--Detectionen_US
dc.titleFacial feature detection and tracking with a 3D constrained local modelen_US
dc.typeThesisen_US
dc.type.qualificationlevelDoctoralen_US
dc.type.qualificationnamePhD Doctor of Philosophyen_US
dc.publisher.institutionThe University of St Andrewsen_US


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