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Activity Number:
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562
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Type:
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Contributed
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Date/Time:
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Thursday, August 6, 2009 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Learning and Data Mining
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| Abstract - #303597 |
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Title:
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Weighted Bidimensional Regression with a Face Matching Application
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Author(s):
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Kendra K. Schmid*+ and David Marx and Ashok Samal
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Companies:
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University of Nebraska Medical Center and University of Nebraska-Lincoln and University of Nebraska-Lincoln
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Address:
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984375 Nebraska Medical Center, Omaha, NE, 68198-4375,
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Keywords:
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Face recognition ; Image mapping ; Landmark data ; Shape analysis ; Shape matching
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Abstract:
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Shape analysis has application in a wide variety of disciplines. One approach focuses on shapes that are represented by predefined landmarks on the object. Some landmarks may be measured with greater precision, exhibit more natural variation, or be more important to the analysis than others. This paper introduces a method for estimating mapping relations or assessing the degree of similarity between two objects represented by a set of two-dimensional spatial coordinates while capturing the spatial variability of each landmark. One possible weighting scheme is suggested, and the effect of weighting is demonstrated through a face matching application. Results indicate that appropriate weighting increases the ability to correctly match two faces, and that weighting has the largest effect when used with the projective transformation.
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