CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

An Enhanced Empirical Method on Choosing the Highest Principal Features and the Number of Hidden Neurons in Principal Component Analysis-Artificial Neural Network Face Recognition based System

by Yasmin Nabil Mousa, Abd - Al- Halim Zekry, Nariman Abd - Alsalam
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 111 - Number 14
Year of Publication: 2015
Authors: Yasmin Nabil Mousa, Abd - Al- Halim Zekry, Nariman Abd - Alsalam
10.5120/19605-1458

Yasmin Nabil Mousa, Abd - Al- Halim Zekry, Nariman Abd - Alsalam . An Enhanced Empirical Method on Choosing the Highest Principal Features and the Number of Hidden Neurons in Principal Component Analysis-Artificial Neural Network Face Recognition based System. International Journal of Computer Applications. 111, 14 ( February 2015), 14-23. DOI=10.5120/19605-1458

@article{ 10.5120/19605-1458,
author = { Yasmin Nabil Mousa, Abd - Al- Halim Zekry, Nariman Abd - Alsalam },
title = { An Enhanced Empirical Method on Choosing the Highest Principal Features and the Number of Hidden Neurons in Principal Component Analysis-Artificial Neural Network Face Recognition based System },
journal = { International Journal of Computer Applications },
issue_date = { February 2015 },
volume = { 111 },
number = { 14 },
month = { February },
year = { 2015 },
issn = { 0975-8887 },
pages = { 14-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume111/number14/19605-1458/ },
doi = { 10.5120/19605-1458 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:47:52.166344+05:30
%A Yasmin Nabil Mousa
%A Abd - Al- Halim Zekry
%A Nariman Abd - Alsalam
%T An Enhanced Empirical Method on Choosing the Highest Principal Features and the Number of Hidden Neurons in Principal Component Analysis-Artificial Neural Network Face Recognition based System
%J International Journal of Computer Applications
%@ 0975-8887
%V 111
%N 14
%P 14-23
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With fast evolving technology, it is necessary to design an efficient security system which can detect unauthorized access on any system. It's needed to implement an extremely secure, economic and perfect system for face recognition that can protect systems from unauthorized access. So, in this paper, a robust face recognition system approach is proposed for feature extraction using Principal Component Analysis, and recognition using Feed Forward Back Propagation Neural Network. The proposed approach gave better results in all aspects including recognition rate, training time, elapsed time and mean square error. The paper shows that when using 80% of the dataset for training, the proposed system can achieve up to 97. 5% recognition rate if correct number of input principal features to the classifier, learning rate, momentum, and number of hidden neurons are used. This algorithm is implemented using Matlab software, and tolerable error value used is superiorly chosen as MSE= 0. 0004.

References
  1. Prachi Agarwal, Naveen Prakash "An Efficient Back Propagation Neural Network Based Face Recognition System Using Haar Wavelet Transform and PCA", International Journal of Computer Science and Mobile Computing (IJCSMC), Vol. 2, Issue. 5, pg. 386 – 395, May 2013.
  2. Navneet Jindal , Vikas Kumar, " Enhanced Face Recognition Algorithm using PCA with Artificial Neural Networks", Electronics and Communication Engineering Department Samalkha Group of Institutes, IJARCSSE, pp 864-872, 2013.
  3. Jyotshana Kanti, Shubha Sharm, "Automated Attendance using Face Recognition based on PCA with Artificial Neural Network", International journal of science and research IJSR, 2012.
  4. Raman Bhati, Sarika Jain, Nilesh Maltare, Durgesh Kumar Mishra, "A Comparative Analysis of Different Neural Networks for Face Recognition Using Principal Component Analysis,Wavelets and Efficient Variable Learning Rate", Int'l Conf. on Computer & Communication Technology, IEEE, pp. 526-531 2010.
  5. Prof. V. P. Kshirsagar, M. R. Baviskar, M. E. Gaikwad, "Face Recognition Using Eigenfaces", Dept. of CSE, Govt. Engineering College, Aurangabad (MS), India, IEEE pp 302-306, 2011.
  6. Kirby, M. , and Sirovich, L. , "Application of the Karhunen-Loeve procedure for the characterization of human faces", IEEE PAMI, Vol. 12, pp. 103-108, (1990).
  7. Matthew A. Turk and Alex P. Pentland "Face Recognition Using Eigenfaces", Vision and Modeling Group, The Media Laboratory Massachusetts Institute of Technology IEEE pp. 586-591, (1991).
  8. Nazish Jamil, Samreen Iqbal, Naveela Iqbal, Fatima Jinnah, "Face Recognition Using Neural Networks", Women University, Rawalpindi, Pakistan, IEEE pp. 277-281, 2001.
  9. Mayank Agarwal, Nikunj Jain, Mr. Manish Kumar and Himanshu Agrawal, "Face Recognition Using Eigen Faces and Artificial Neural Network", International Journal of Computer Theory and Engineering, pp 624-629 August, 2010.
  10. P. Latha, L. Ganesan, and S. Annadurai," Face Recognition using Neural Networks", Signal Processing: An International Journal (SPIJ), pp 153-160.
  11. Borut Batagelj and Franc Solina, "Face Recognition in Different Subspaces: A Comparative Study", University of Ljubljana, Faculty of Computer and Information Science.
  12. Simon Gangl, Domen Mongus, and Borut Z?alik, "Comparison of face recognition algorithms in terms of the learning set selection", Laboratory for Geometric Modelling and Multimedia Algorithms, SlCESCG, the Central European Seminar on Computer Graphics, 2010.
  13. f. Ujval Chaudhary, Chakoli Mateen Mubarak, Abdul Rehman, Ansari Riyaz, and Shaikh Mazhar, "Face Recognition Using PCA-BPNN Algorithm", International Journal of Modern Engineering Research (IJMER), pp. 1366-1370, Vol. 2, Issue. 3, May-June 2012.
  14. Vinodpuri Rampuri Gosavi, G. S. Mandal's, "A New Era of Face Recognition", International Journal of Engineering Research & Technology (IJERT) Vol. 1 Issue 8, October – 2012.
  15. Pavan k. Yalamanchili and Bhanu Durga Paladugu, " ECE847 : Digital Image Processing, Comparative Study of Face Recognition Algorithms", Department of Electrical Engineering, Clemson University, South Carolina, USA.
  16. Jorge Orts," ECE533 – Image Processing Project on Face Recognition Techniques", , the university of Wisconsin Madison.
  17. Kresimir Delac , Mislav Grgic , Panos Liatsis, "Appearance-based Statistical Methods for Face Recognition", International Symposium ELMAR, pp 151-158, June 2005.
  18. Shahrin Azuan Nazeer and Marzuki Khalid, "PCA-ANN Face Recognition System based on Photometric Normalization Techniques" Telekom Research & Development Sdn. Bhd. , University Technology, Malaysia, Source: State of the Art in Face Recognition, Book edited by: Dr. Mario I. Chacon M. , ISBN -3-902613-42-4, pp. 250, , I-Tech, Vienna, Austria, Jan 2009.
  19. A. Swathi1 , Dr. R. Pugazendi, and , K. S. Rangasamy, "Face Recognition Using Multi-Support Vector Machines", International Journal of Computer Trends and Technology (IJCTT) – volume 4 Issue 8pp. 2556-2560– August 2013.
  20. Fábio Abrantes Diniz, Francisco Milton Mendes Neto, Francisco das Chagas Lima Júnior, and Laysa Mabel de Oliveira Fontes, "A Facial Recognition System Based on Techniques of Principal Component Analysis and Autofaces with K-NN, K-Star and Random Forest Classifiers", Research Notes in Information Science (RNIS), Volume12, pp. 7-14, April 2013.
  21. Matthew A. Turk and Alex P. Pentland " Eigenfaces for recognition", Vision and Modeling Group, The Media Laboratory Massachusetts Institute of Technology, journal of cognitive neuro science, pp 71-86, 1991.
  22. Jamshid Nazari, Implementation of back-propagation neural networks with Matlab,. ECE Technical Reports. Paper 275, http://docs. lib. purdue. edu/ecetr/275, 1992.
  23. E. Hosseini Aria, J. Amini, M. R. Saradjian, "Back Propagation Neural Network for Classification of IRS-1D Satellite Images, Department of geomantics, Faculty of Engineering, Tehran University, Iran, Jamebozorg G. , National Cartographic Center (NCC), Tehran, Iran.
  24. Mohamed Elwakeel, Ziyad Shaaban, " Face Recognition based on Haar wavelet transform and Principal Component analysis via Levenberg-Marquardt back propagation neural network", European Journal of scientific Research, pp 25-31, 2010.
  25. http://www. rohan. sdsu. edu/doc/matlab/toolbox/nnet/backpr54. html.
  26. http://in. mathworks. com/help/nnet/ref/trainlm. html.
Index Terms

Computer Science
Information Sciences

Keywords

Face recognition Principal component analysis (PCA) Eigenvector Eigenface Artificial Neural network (ANN).

Powered by PhDFocusTM