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Permanent link (DOI): https://doi.org/10.7939/R34Q0B

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Face Recognition using Local Descriptors and Different Classification Schemas Open Access

Descriptions

Other title
Subject/Keyword
local descriptors
ensemble classification
FERET
face recognition
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Liu,Ting
Supervisor and department
Pedrycz, Witold (Electrical and Computer Engineering)
Reformat, Marek (Electrical and Computer Engineering)
Examining committee member and department
Musilek, Petr (Electrical and Computer Engineering)
Reformat, Marek (Electrical and Computer Engineering)
Pedrycz,Witold (Electrical and Computer Engineering)
Kuru,Ergun (Civil and Environmental Engineering)
Department
Department of Electrical and Computer Engineering
Specialization
Software Engineering and Intelligent System
Date accepted
2013-01-30T12:54:19Z
Graduation date
2013-06
Degree
Master of Science
Degree level
Master's
Abstract
There are two main activities in a face recognition practice: representation and classification. The main focus of this work is an analysis of image representation methods leading to better image classification scores. This study applies different feature descriptors and image segmentation techniques of image depiction, and investigates their influence on the classification results. We have proposed a number of single and ensemble classification approaches. For single classification approaches, we have considered different segmentation-based techniques of image processing, with weight-based strategies showing the most promising outcomes. In the case of ensemble-based classification algorithms we have investigated multiple criteria of importance focusing on ranking of candidates, as well as on segments and features sorted based on their prominence. We assessed and compared experimental results obtained for the FERET database. The most significant experimental results have been obtained for weighted-based strategy for single classification.
Language
English
DOI
doi:10.7939/R34Q0B
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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