Face recognition using eigenfaces and smooth support vector machine

Face is one of the unique features of human body which has complicated characteristic.Facial features (eyes, nose, and mouth) can be used for face recognition. Support Vector Machine (SVM) is a new algorithm of data mining technique, recently received increasing popularity in machine learning commu...

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Main Author:	Mhd, Furqan
Format:	Undergraduates Project Papers
Language:	English
Published:	2011
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/2732/ http://umpir.ump.edu.my/id/eprint/2732/ http://umpir.ump.edu.my/id/eprint/2732/1/MHD_FURQAN.PDF

id	ump-2732
recordtype	eprints
spelling	ump-27322015-03-03T07:58:11Z http://umpir.ump.edu.my/id/eprint/2732/ Face recognition using eigenfaces and smooth support vector machine Mhd, Furqan TA Engineering (General). Civil engineering (General) Face is one of the unique features of human body which has complicated characteristic.Facial features (eyes, nose, and mouth) can be used for face recognition. Support Vector Machine (SVM) is a new algorithm of data mining technique, recently received increasing popularity in machine learning community. The Smooth Support Vector Machine (SSVM) is a further development of the SVM. Smoothing methods, extensively used for solving important mathematical programming problems and applications. They are applied here to generate and solve an unconstrained smooth reformulation of the support vector machine for pattern classification using a completely arbitrary kernel. Such reformulation is a SSVM. A Newton-Armijo algorithm for solving the SSVM converges globally and quadratically. Here, the SSVM is applied as a classifier on face recognition using eigenfaces as the input. In relation to that, Jacobi's method is used to compute the eigenvalues and eigenvectors. The eigenfaces is projected onto human faces to identify the features vector. These significant features vector are further refine using Gaussian kernel. An experiment using standard data set from AT&T Laboratories Cambridge with 400 samples of human faces is conducted. In addition to that,tenfold cross validation is used to validate the performance of the proposed method. The results have revealed that the accuracy rate of 99.68% is achieved. 2011-02 Undergraduates Project Papers NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/2732/1/MHD_FURQAN.PDF Mhd, Furqan (2011) Face recognition using eigenfaces and smooth support vector machine. Faculty of Computer System and Software Engineering, Universiti Malaysia Pahang. http://iportal.ump.edu.my/lib/item?id=chamo:58740&theme=UMP2
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Mhd, Furqan Face recognition using eigenfaces and smooth support vector machine
description	Face is one of the unique features of human body which has complicated characteristic.Facial features (eyes, nose, and mouth) can be used for face recognition. Support Vector Machine (SVM) is a new algorithm of data mining technique, recently received increasing popularity in machine learning community. The Smooth Support Vector Machine (SSVM) is a further development of the SVM. Smoothing methods, extensively used for solving important mathematical programming problems and applications. They are applied here to generate and solve an unconstrained smooth reformulation of the support vector machine for pattern classification using a completely arbitrary kernel. Such reformulation is a SSVM. A Newton-Armijo algorithm for solving the SSVM converges globally and quadratically. Here, the SSVM is applied as a classifier on face recognition using eigenfaces as the input. In relation to that, Jacobi's method is used to compute the eigenvalues and eigenvectors. The eigenfaces is projected onto human faces to identify the features vector. These significant features vector are further refine using Gaussian kernel. An experiment using standard data set from AT&T Laboratories Cambridge with 400 samples of human faces is conducted. In addition to that,tenfold cross validation is used to validate the performance of the proposed method. The results have revealed that the accuracy rate of 99.68% is achieved.
format	Undergraduates Project Papers
author	Mhd, Furqan
author_facet	Mhd, Furqan
author_sort	Mhd, Furqan
title	Face recognition using eigenfaces and smooth support vector machine
title_short	Face recognition using eigenfaces and smooth support vector machine
title_full	Face recognition using eigenfaces and smooth support vector machine
title_fullStr	Face recognition using eigenfaces and smooth support vector machine
title_full_unstemmed	Face recognition using eigenfaces and smooth support vector machine
title_sort	face recognition using eigenfaces and smooth support vector machine
publishDate	2011
url	http://umpir.ump.edu.my/id/eprint/2732/ http://umpir.ump.edu.my/id/eprint/2732/ http://umpir.ump.edu.my/id/eprint/2732/1/MHD_FURQAN.PDF
first_indexed	2023-09-18T21:56:48Z
last_indexed	2023-09-18T21:56:48Z
_version_	1777414133301379072

Face recognition using eigenfaces and smooth support vector machine

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