test Browse by Author Names Browse by Titles of Works Browse by Subjects of Works Browse by Issue Dates of Works
       

Advanced Search
Home   
 
Browse   
Communities
& Collections
  
Issue Date   
Author   
Title   
Subject   
 
Sign on to:   
Receive email
updates
  
My Account
authorized users
  
Edit Profile   
 
Help   
About T-Space   

T-Space at The University of Toronto Libraries >
School of Graduate Studies - Theses >
Master >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1807/18902

Title: Linear Feature Extraction with Emphasis on Face Recognition
Authors: Mahanta, Mohammad Shahin
Advisor: Plataniotis, Konstantinos N.
Department: Electrical and Computer Engineering
Keywords: Feature Extraction
Classification
Face Recognition
Sufficient Statistic
Issue Date: 15-Feb-2010
Abstract: Feature extraction is an important step in the classification of high-dimensional data such as face images. Furthermore, linear feature extractors are more prevalent due to computational efficiency and preservation of the Gaussianity. This research proposes a simple and fast linear feature extractor approximating the sufficient statistic for Gaussian distributions. This method preserves the discriminatory information in both first and second moments of the data and yields the linear discriminant analysis as a special case. Additionally, an accurate upper bound on the error probability of a plug-in classifier can be used to approximate the number of features minimizing the error probability. Therefore, tighter error bounds are derived in this work based on the Bayes error or the classification error on the trained distributions. These bounds can also be used for performance guarantee and to determine the required number of training samples to guarantee approaching the Bayes classifier performance.
URI: http://hdl.handle.net/1807/18902
Appears in Collections:Master
The Edward S. Rogers Sr. Department of Electrical & Computer Engineering - Master theses

Files in This Item:

File Description SizeFormat
Mahanta_MohammadShahin_200911_MASc_thesis.pdf1.84 MBAdobe PDF
View/Open

This item is licensed under a Creative Commons License
Creative Commons

Items in T-Space are protected by copyright, with all rights reserved, unless otherwise indicated.

uoft