Face Recognition with PCA and KPCA using Elman Neural Network and SVM
In this paper, in order to categorize ORL database face
pictures, principle Component Analysis (PCA) and Kernel Principal
Component Analysis (KPCA) methods by using Elman neural
network and Support Vector Machine (SVM) categorization methods
are used. Elman network as a recurrent neural network is proposed
for modeling storage systems and also it is used for reviewing the
effect of using PCA numbers on system categorization precision rate
and database pictures categorization time. Categorization stages are
conducted with various components numbers and the obtained results
of both Elman neural network categorization and support vector
machine are compared. In optimum manner 97.41% recognition
accuracy is obtained.
[1] Sh.Yan, Q.Yang,L.Zhang, X.Tang and H-J Zhang,"Multilinear
Discriminant Analysis for Face recognition",IEEE Transactions on
Image Processing,, JANUARY 2007:PP 212-220
[2] Charles F.van Loan, "Lecture 2. Tensor Unfolding", Transition to
computational multi linear Algebra, The Gene Golub SIAM summer
School 2010 Selva di Fasano, Brindisi, Italy
[3] Ramirez, L.; Durdle, N.G.; Raso, V.J.; Hill, D.L., A,-- support vector
machines classifier to assess the severity of audio pathic scoliosis from
surface topography--. IEEE Transactions on Information Technology in
Biomedicine,2006, pp. 84-91.
[4] Ivanna K. Timotius, Iwan Setyawan, and Andreas A. Febrianto ,-- Face
Recognition between Two Person using Kernel Principal Component
Analysis and support Vector Machines--, International Journal on
Electrical Engineering and Informatics - Vol 2, No 1, 2010.
[5] Bernhard. schoolkopf ,Alexanders. Molaand Klaus- Robert.
muller,--nonlinear component analysis as a kernel eigenvalue
problem--,Max-Planck-
Institutf├╝rbiologischeKybernetikArbeitsgruppeB├╝lthoSpemannstraße 38
* 72076 T├╝bingen. Germany,December 1996.
[6] Omar Faruqe , Al MehediHasan ,-- Face Recognition Using PCA and
SVM--, Dept. of Computer Science &EngineeringRajshahiUniversity of
Engineering &Technology,Rajshahi, Bangladesh, 2009
[7] A. Lima, H. Zen, Y. Nankaku, C. Miyajima, K. Tokuda, T.
Kitamura,--On the Use of Kernel PCA for Feature Extraction in Speech
Recognition--, Proceeding of Euro Speech, Sep. 2003:pp. 2625-2628
[8] B. Scholkopf, A. Smola, and K. R. Muller, Nonlinear component
analysisas a kernel eigenvalue problem, Neural Comput., vol. 10, no.
5,1998:pp. 1299-1319,
[9] Demuth.H, Beale.M, "Neural Network Toolbox for Use with
MATLAB", User-s Guide, 2000;1-9.
[10] Elman.J, "Finding Structure in Time", Cognitive Science, 1990:14;179-
211.
[1] Sh.Yan, Q.Yang,L.Zhang, X.Tang and H-J Zhang,"Multilinear
Discriminant Analysis for Face recognition",IEEE Transactions on
Image Processing,, JANUARY 2007:PP 212-220
[2] Charles F.van Loan, "Lecture 2. Tensor Unfolding", Transition to
computational multi linear Algebra, The Gene Golub SIAM summer
School 2010 Selva di Fasano, Brindisi, Italy
[3] Ramirez, L.; Durdle, N.G.; Raso, V.J.; Hill, D.L., A,-- support vector
machines classifier to assess the severity of audio pathic scoliosis from
surface topography--. IEEE Transactions on Information Technology in
Biomedicine,2006, pp. 84-91.
[4] Ivanna K. Timotius, Iwan Setyawan, and Andreas A. Febrianto ,-- Face
Recognition between Two Person using Kernel Principal Component
Analysis and support Vector Machines--, International Journal on
Electrical Engineering and Informatics - Vol 2, No 1, 2010.
[5] Bernhard. schoolkopf ,Alexanders. Molaand Klaus- Robert.
muller,--nonlinear component analysis as a kernel eigenvalue
problem--,Max-Planck-
Institutf├╝rbiologischeKybernetikArbeitsgruppeB├╝lthoSpemannstraße 38
* 72076 T├╝bingen. Germany,December 1996.
[6] Omar Faruqe , Al MehediHasan ,-- Face Recognition Using PCA and
SVM--, Dept. of Computer Science &EngineeringRajshahiUniversity of
Engineering &Technology,Rajshahi, Bangladesh, 2009
[7] A. Lima, H. Zen, Y. Nankaku, C. Miyajima, K. Tokuda, T.
Kitamura,--On the Use of Kernel PCA for Feature Extraction in Speech
Recognition--, Proceeding of Euro Speech, Sep. 2003:pp. 2625-2628
[8] B. Scholkopf, A. Smola, and K. R. Muller, Nonlinear component
analysisas a kernel eigenvalue problem, Neural Comput., vol. 10, no.
5,1998:pp. 1299-1319,
[9] Demuth.H, Beale.M, "Neural Network Toolbox for Use with
MATLAB", User-s Guide, 2000;1-9.
[10] Elman.J, "Finding Structure in Time", Cognitive Science, 1990:14;179-
211.
@article{"International Journal of Information, Control and Computer Sciences:52267", author = "Hossein Esbati and Jalil Shirazi", title = "Face Recognition with PCA and KPCA using Elman Neural Network and SVM", abstract = "In this paper, in order to categorize ORL database face
pictures, principle Component Analysis (PCA) and Kernel Principal
Component Analysis (KPCA) methods by using Elman neural
network and Support Vector Machine (SVM) categorization methods
are used. Elman network as a recurrent neural network is proposed
for modeling storage systems and also it is used for reviewing the
effect of using PCA numbers on system categorization precision rate
and database pictures categorization time. Categorization stages are
conducted with various components numbers and the obtained results
of both Elman neural network categorization and support vector
machine are compared. In optimum manner 97.41% recognition
accuracy is obtained.", keywords = "Face recognition, Principal Component Analysis,
Kernel Principal Component Analysis, Neural network, Support
Vector Machine.", volume = "5", number = "10", pages = "1097-5", }