Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.722204
Title: Dense surface models of the human face
Author: Hutton, Tim J.
Awarding Body: University of London
Current Institution: University College London (University of London)
Date of Award: 2004
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Abstract:
This thesis describes and evaluates Dense Surface Models (DSMs), a new technique for building point distribution models of surfaces, from raw input data. DSMs can be used on data from a wide range of surface acquisition systems without preprocessing since they do not require that the surfaces be closed or even locally manifold, and can cope well with holes and spikes in the surfaces. This is an advantage over comparable techniques, which impose such constraints on the input. The core of the DSM algorithm is as follows. A dense correspondence is made between the surfaces using thin-plate spline warping guided by means of a small set of hand-placed landmarks. The area of interest is automatically defined by a threshold on a measure of the closeness of the correspondence at each point. A point distribution model is then built using the vertices from the trimmed and densely-corresponded surfaces. The key benefit of using models of the whole surface is illustrated by the large improvement in classification on face shape that is obtained when using DSMs as compared to landmark-based geometric morphometrics. This is demonstrated by testing classification by gender and also by congenital anomaly where facial growth and form is abnormal. The latter is currently the primary application of DSMs. The use of DSMs for automatically fitting to new scans is evaluated for robustness and accuracy. Methods for analyzing continuous and discrete parameters such as age and gender are presented and evaluated. The incorporation of grey-level information with the shape information is also possible, and is explored.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.722204  DOI: Not available
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