Multiscale feature extraction and matching with applications to 3D face recognition and 2D shape warping
Item
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Title
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Multiscale feature extraction and matching with applications to 3D face recognition and 2D shape warping
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Identifier
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d_2009_2013:96ad32b8e589:11410
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identifier
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11140
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Creator
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Fadaifard, Hadi,
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Contributor
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George Wolberg
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Date
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2011
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Language
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English
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Publisher
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City University of New York.
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Subject
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Computer science | face recognition | shape deformation | shape matching | surface registration
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Abstract
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Shape matching is defined as the process of computing a dissimilarity measure between shapes. Partial 3D shape matching refers to a more difficult subproblem that deals with measuring the dissimilarity between partial regions of 3D objects. Despite a great deal of attention drawn to 3D shape matching in the fields of computer vision and computer graphics, partial shape matching applied to objects of arbitrary scale remains a difficult problem.;This work addresses the problem of partial 3D shape matching with no assumptions about the scale factors of the input objects. We introduce a multiscale feature extraction and matching technique that employs a new scale-space based representation for 3D surfaces. The representation is shown to be insensitive to noise, computationally efficient, and capable of automatic scale selection. Applications of the proposed representation are presented for automatic 3D surface registration, face detection, and face recognition. Test results involving two well-known 3D face datasets consisting of several thousand scanned human faces demonstrate that the performance of our recognition system is superior over competing methods.;Estimating differential surface attributes, such as normals and curvatures, plays an important role in the performance of 3D matching systems. Noise in the data, however, poses the main challenge in estimating these attributes. Surface reconstruction methods, such as Moving Least Squares (MLS), help in minimizing the effects of noise. In this work, we also review the MLS approach for surface reconstruction, and show how the input noise affects the estimated differential attributes of the surface. We demonstrate how these results, together with statistical hypothesis testing, may be used to determine the smallest neighborhood size needed to estimate surface attributes.;MLS reconstruction and the discrete Laplace-Beltrami operator are well-known geometric tools that have a wide range of applications. In addition to their prominent use in our 3D work, we describe a novel use of these tools in a 2D shape deformation system for retargeting garments among arbitrary poses.
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Type
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dissertation
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Source
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2009_2013.csv
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degree
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Ph.D.
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Program
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Computer Science
