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...
| Main Author: | |
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| Format: | Undergraduates Project Papers |
| Language: | English |
| Published: |
2011
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/2732/ http://umpir.ump.edu.my/id/eprint/2732/1/MHD_FURQAN.PDF |
| _version_ | 1848817122238529536 |
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| author | Mhd, Furqan |
| author_facet | Mhd, Furqan |
| author_sort | Mhd, Furqan |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T01:16:45Z |
| format | Undergraduates Project Papers |
| id | ump-2732 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T01:16:45Z |
| publishDate | 2011 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-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. |
| spellingShingle | TA Engineering (General). Civil engineering (General) Mhd, Furqan Face recognition using eigenfaces and smooth support vector machine |
| title | 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_short | Face recognition using eigenfaces and smooth support vector machine |
| title_sort | face recognition using eigenfaces and smooth support vector machine |
| topic | TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/2732/ http://umpir.ump.edu.my/id/eprint/2732/1/MHD_FURQAN.PDF |