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Face Recognition Through Regret Minimization.
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Abstract |
Abstract
Face Recognition is an important problem for Artificial Intelligence Researchers, with applications to law enforcement, medicine and entertainment. Many different approaches to the problem have been suggested most approaches can be categorized as being either Holistic or Local. Recently, local approaches have shown some gains. This thesis presents a system for embedding a holistic algorithm into a local framework. The system proposed builds on the concept of Regional Voting, to create Weighted Regional Voting which divides the face images to be classified into regions, performs classification on each region, and finds the final classification through a weighted majority vote on the regions. Three different weighting schemes taken from the field of Regret Minimization are suggested, and their results compared. Weighted Regional Voting is shown to improve upon unweighted Regional Voting in every case, and to outperform or equal many modern face recognition algorithms. --P. ii. |
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Persons |
Persons
Author (aut): Yule, Daniel
Thesis advisor (ths): Chen, Liang
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Degree Name
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Department |
Department
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DOI |
DOI
https://doi.org/10.24124/2011/bpgub798
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Collection(s)
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Degree granting institution (dgg): University of Northern British Columbia
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Library of Congress Classification |
Library of Congress Classification
TA1650 .Y95 2011
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Extent |
Extent
Number of pages in document: 73
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Physical Form
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Handle
Handle placeholder
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ISBN |
ISBN
978-0-494-75163-3
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Use and Reproduction |
Use and Reproduction
Copyright retained by the author.
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Rights Statement |
Rights Statement
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unbc_16161.pdf2.57 MB
28293-Extracted Text.txt97.24 KB
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Language |
English
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Face Recognition Through Regret Minimization.
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application/pdf
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2698913
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