3D Integral Invariant Signatures And Their Application on Face Recognition

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dc.contributor.advisor Hamid Krim, Committee Chair en_US
dc.contributor.advisor Irina Kogan, Committee Member en_US
dc.contributor.advisor Michael Escuti, Committee Member en_US
dc.contributor.advisor Griff Bilbro, Committee Member en_US
dc.contributor.author Feng, Shuo en_US
dc.date.accessioned 2010-04-02T19:00:26Z
dc.date.available 2010-04-02T19:00:26Z
dc.date.issued 2007-09-17 en_US
dc.identifier.other etd-09102007-153942 en_US
dc.identifier.uri http://www.lib.ncsu.edu/resolver/1840.16/4754
dc.description.abstract Curves are important features in computer vision and pattern recognition, and their classification under a variety of transformations, such as Euclidean, affine or projective, poses a great challenge. Invariant features of these curves turn out to be crucial to simplifying any classification procedure. This, as a result, has recently led to a renewed research interest in transformation invariants. In this thesis, new explicit formulae for integral invariants for curves in 3D with respect to the special and the full affine groups are presented.The development of the 3D integral invariant are based on an inductive approach in terms of Euclidean invariants. For the first time, a clear geometric interpretation of both 2D and 3D integral invariants is presented. Since integration attenuates the effects of noise, integral invariants have advantages in computer vision applications. We use integral invariants to construct global and local signatures that characterize curves up to the special affine transformations, subsequently extended to the full affine group. Global Signatures are independent of parameterization, and Local Signatures are independent of both parameterizationa and initial point selection. We analyze the robustness of these invariants in their application to the problem of classification of noisy spatial curves extracted as characteristics from a 3D object. Our investigation of 2D and 3D integral invariants and signatures, originally motivated by Biometrics applications, are successfully implemented and applied to face recognition to eliminate the effects of pose and facial expression. A high recognition performance rate of 95% is achieved in the test with a large face data set. 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 face recognition en_US
dc.subject integral invariant signature en_US
dc.subject 3D integral invariant en_US
dc.title 3D Integral Invariant Signatures And Their Application on Face Recognition en_US
dc.degree.name PhD en_US
dc.degree.level dissertation en_US
dc.degree.discipline Electrical Engineering en_US

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