3D face recognition from range images

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dc.contributor.advisor Griff Bilbro, Committee Member en_US
dc.contributor.advisor Wesley Snyder, Committee Member en_US
dc.contributor.advisor Hamid Krim, Committee Chair en_US
dc.contributor.author Miao, Shun en_US
dc.date.accessioned 2010-08-19T18:19:04Z
dc.date.available 2010-08-19T18:19:04Z
dc.date.issued 2010-05-03 en_US
dc.identifier.other etd-04022010-215233 en_US
dc.identifier.uri http://www.lib.ncsu.edu/resolver/1840.16/6295
dc.description.abstract In this thesis, we explore the statistical and geometrical behavior of uncontrolled parameters of human face, including both rigid transform caused by head pose and non-rigid transform caused by facial expression. We focus on developing 3D facial recognition schemes that can be robust for these uncontrolled parameters. This thesis presents a novel 3D face recognition method by means of the evolution of iso-geodesic distance curves. Specifically, the proposed method compares two neighboring iso-geodesic distance curves, and formalizes the evolution between them as a one-dimensional function, named evolution angle function, which is Euclidean invariant. The novelty of this paper consists in formalizing 3D face by an evolution angle functions, and in computing the distance between two faces by that of two functions. Experiments on Face Recognition Grand Challenge (FRGC) ver2.0 shows that our approach works very well on neutral faces. By introducing a weight function, we also show a promising result on non-neutral face database. A 3D surface segmentation scheme is developed to detect the partial similarity between facial images. The proposed algorithm is based on iterative closest point (ICP) algorithm, which uses mean square distance as the cost function and is not able to detect partial similarities. The presented thesis make an improvement of ICP algorithm by iteratively removing points contributing largest error, and the remaining area of surface can be shown to be the partial similarity between two surface en_US
dc.rights I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dis sertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. en_US
dc.subject geodesic en_US
dc.subject segmentation en_US
dc.subject face recognition en_US
dc.subject pattern recognition en_US
dc.title 3D face recognition from range images en_US
dc.degree.name MS en_US
dc.degree.level thesis en_US
dc.degree.discipline Electrical Engineering en_US

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