CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Face Identification based on Contourlet Transform and Multi-layer Perceptron Classifier

by Ali Fattah Hassoon, Maher K. AL-Azzawi, Tariq Tashan
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 133 - Number 2
Year of Publication: 2016
Authors: Ali Fattah Hassoon, Maher K. AL-Azzawi, Tariq Tashan
10.5120/ijca2016907384

Ali Fattah Hassoon, Maher K. AL-Azzawi, Tariq Tashan . Face Identification based on Contourlet Transform and Multi-layer Perceptron Classifier. International Journal of Computer Applications. 133, 2 ( January 2016), 12-18. DOI=10.5120/ijca2016907384

@article{ 10.5120/ijca2016907384,
author = { Ali Fattah Hassoon, Maher K. AL-Azzawi, Tariq Tashan },
title = { Face Identification based on Contourlet Transform and Multi-layer Perceptron Classifier },
journal = { International Journal of Computer Applications },
issue_date = { January 2016 },
volume = { 133 },
number = { 2 },
month = { January },
year = { 2016 },
issn = { 0975-8887 },
pages = { 12-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume133/number2/23757-2016907384/ },
doi = { 10.5120/ijca2016907384 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:30:00.361346+05:30
%A Ali Fattah Hassoon
%A Maher K. AL-Azzawi
%A Tariq Tashan
%T Face Identification based on Contourlet Transform and Multi-layer Perceptron Classifier
%J International Journal of Computer Applications
%@ 0975-8887
%V 133
%N 2
%P 12-18
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Useful properties of the Contourlet Transform (CT) are exploited in this paper to investigate more discriminant features to enhance the face identification performance. In this paper a face identification system is suggested based on CT, and Multi-Layer Perceptron (MLP) Classifier. The main reasons behind using the CT are: First, the CT supports progressive data compression/expansion, hence it is used for image compression. Second, since the features in human face are not just horizontal or vertical. CT is utilized for feature extraction because it is a genuine 2-D transform that can capture the edge information in all directions. After decomposing an image by CT, low and high frequency coefficients of CT are calculated in different scales and various angles. The frequency coefficients are utilized as an input feature vector for a neural network classifier. Simple feed forward MLP neural network is used to achieve the identification process. The network parameters are tuned to optimal values, in order to produce fair comparison between different types of feature vectors. To evaluate the algorithm performance five different databases are used. Some of them of high variability, which examines the algorithm robustness against variability. In addition, the proposed algorithm is evaluated using a generated database which composes two databases. Then the suggested method is compared to other classical feature-based methods such as, wavelet, and Principle Component Analysis (PCA). The results indicate that the CT-based method has better identification rate, and is faster than the Wavelet-based and the PCA-based methods. This is due to the high sparsity of the CT and its efficient capability of compression. An average identification rate of 93.94% is obtained for the CT-based method, 85.12% for the Wavelet and 79.96% for the PCA.

References
  1. Yanjun Yan, Rajani Muraleedharan, Xiang Ye and Lisa Ann Osadciw, " Contourlet Based Image Compression for Wireless Communication in Face Recognition System", IEEE International Conference on Communications, May,2008, pp. 505-509.
  2. Walid Riad Boukabou and Ahmed Bouridane ” Contourlet-Based Feature Extraction with PCA for Face Recognition” The Institute of Electronics, Communications and Information Technology (ECIT).
  3. Majid Iranpour Mobarakeh, Mehran Emadi, Majid Emadi,"FRBF Neural Network base for Face Recognition using Zernike Moments and PCA", International Journal of Computer Applications, vol. 125, no. 2, pp. 10-14, 2015.
  4. Turk, M., Pentland, A. ”Eigenfaces for Recognition,” Journal of Cognitive Neuroscience. vol. 3, no. 1, pp. 71-86, 1991.
  5. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711720, Jul. 1997.
  6. Bartlett, M.S., Movellan, J.R., Sejnowski, T.J. ”Face Recognition by Independent Component Analysis,” IEEE Transactions on Neural Netwprks, vol. 13, no. 6, pp. 1450-1464, 2002.
  7. N.G.Chitaliya, A.I.Trivedi, “Feature Extraction using Wavelet-PCA and Neural network for application of Object Classification & Face Recognition,” ICCEA, volume 1, pp.510-514, 2010.
  8. XUEBIN XU, DEYUN ZHANG, XINMAN ZHANG , ” An efficient method for human face recognition using nonsubsampled contourlet transform and support vector machine ” , Optica Applicata, Vol. XXXIX, No. 3, 2009.
  9. Starack J.L.,Candes E.J., Donoho D.L.,” The Curvelet transform for image denoising”, IEEE Transactions on Image Processing 11(6), 2002, pp. 670–684
  10. Tanaya Mandal, Angshul Majmudar, Q.M.Jonathan W U,” Face recognition by Curvelet based feature extraction”, International Conference on Intelligent Automation and Robotics, LNCS 4633, 2007, pp. 806–817.
  11. Xuebin Xu, Deyun Zhang, Xinman Zhan Zhang,”An efficient method for human face recognition using nonsubsampled Contourlet transform and support vector machine * Optica Applicata, Vol. XXXIX, No. 3, 2009pp 601-615.
  12. DO M.N., Vetterli M., “The Contourlet transform: an efficient directional multiresolution image representation”, IEEE Transactions on Image Processing 14(12), 2005, pp. 2091 –2106.
  13. Zhou J., Cunha A.L., M.N. Do.,” Nonsubsampled Contourlet transform: construction and application in enhancement”, Proceedings – International Conference on Image Processing, ICIP 2005, Vol. 1, pp,469 –472
  14. LU Y., Do M.N., “A new Contourlet transform with sharp frequency localization”, IEEE International Conference on Image Processing, 2006, pp. 1629– 1632.
  15. Hanglong YU, Shengsheng YU et al., “An image compression scheme based on modified Contourlet transform”, Computer Engineering and Application 41(1), 2005, pp. 40– 43.
  16. Ch. Srinivasa Rao, S.Srinivas Kumar, B.N.Chatterji ” Content Based Image Retrieval using Contourlet Transform” – ICGST-GVIP Journal, volume 7(3), November 2007.
  17. Jun Yan, Muraleedharan R., Xiang YE, Osadciw L.A., “Contourlet based image compression for wireless communication in face recognition system”, IEEE International Conference on Communication, 2008, pp. 505–509.
  18. Mohanad A.M. Abukmeil and Dr. Hatem ELaydi, ” Palmprint Recognition System By Using Contourlets Transform And Artificial Neural Network” The Islamic University Of Gaza, 2013.
  19. Do,M.N. and Vetterli, M. ”The Contourlet Transform: An Efficient Directional Multiresolution Image Representation”, IEEE Transactions on Image Processing, vol. 14, no. 12, pp. 2091-2106, 2005.
  20. N.G.Chitaliya and Prof.A.I.Trivedi , ” An Efficient Method for Face Feature Extraction and Recognition based on Contourlet Transform and Principal Component Analysis using Neural Network”, International Journal of Computer Applications (0975 – 8887) Volume 6– No.4, September 2010.
  21. http://www.face-rec.org/databases/
  22. Ali Ibrahim Abbas, Prof. Dr. Waleed Ameen Mahmoud, Ass. Prof. Nuha Abdul-Saheb Alwan” Face Identification Using Multiwavelet-based Neural Network ” A Thesis Submitted to the Department of Electrical Engineering in the University of Baghdad . September 2010.
Index Terms

Computer Science
Information Sciences

Keywords

Contourlet Transform Face Identification Multi-Layer Perceptron Neural Network.

Powered by PhDFocusTM