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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/2732/1/MHD_FURQAN.PDF
http://umpir.ump.edu.my/id/eprint/2732/
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spelling my.ump.umpir.27322021-07-07T06:19:10Z 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.
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
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/2732/1/MHD_FURQAN.PDF
http://umpir.ump.edu.my/id/eprint/2732/
_version_ 1705056895943835648
score 13.211869

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