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Reseach Article

FRBF Neural Network base for Face Recognition using Zernike Moments and PCA

by Majid Iranpour Mobarakeh, Mehran Emadi, Majid Emadi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 125 - Number 2
Year of Publication: 2015
Authors: Majid Iranpour Mobarakeh, Mehran Emadi, Majid Emadi
10.5120/ijca2015905834

Majid Iranpour Mobarakeh, Mehran Emadi, Majid Emadi . FRBF Neural Network base for Face Recognition using Zernike Moments and PCA. International Journal of Computer Applications. 125, 2 ( September 2015), 10-14. DOI=10.5120/ijca2015905834

@article{ 10.5120/ijca2015905834,
author = { Majid Iranpour Mobarakeh, Mehran Emadi, Majid Emadi },
title = { FRBF Neural Network base for Face Recognition using Zernike Moments and PCA },
journal = { International Journal of Computer Applications },
issue_date = { September 2015 },
volume = { 125 },
number = { 2 },
month = { September },
year = { 2015 },
issn = { 0975-8887 },
pages = { 10-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume125/number2/22403-2015905834/ },
doi = { 10.5120/ijca2015905834 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:14:57.189753+05:30
%A Majid Iranpour Mobarakeh
%A Mehran Emadi
%A Majid Emadi
%T FRBF Neural Network base for Face Recognition using Zernike Moments and PCA
%J International Journal of Computer Applications
%@ 0975-8887
%V 125
%N 2
%P 10-14
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper a method to face recognition in digital images based on statistical features and fuzzy neural networks will be introduced. In order to increase system performance, and analysis of the basic components, Zernike moments used as features have been used and various combinations of these features have been introduced. Work is based on the use of fuzzy neural network of FRBF with a teaching method based on fuzzy training in face recognition with high accuracy. In FHLA algorithm used in learning, in addition to determining weights between hidden layer and output layer parameters, including center RBF neurons and the width shall be determined during the training process. In this way of education, initial values of parameters using fuzzy logic and troubleshooting methods and fuzzy clustering hidden layer neurons are obtained by number of FCM techniques. To determine the final values of communication parameters and weights, the gradient method and the LLS is used as optimization methods. Test results show that this technique has very good accuracy in identifying faces on the database composed of 1000.

References
  1. R. K. Yin, Face recognition by brain-injured patients: A dissociable ability?, Neuropsychologia, , Vol. 8, n. 4, pp. 359-402, 1970.
  2. D. Maltoni, D. Maio, A.K. Jain, S. Parbhakar, Hand book of fingerprint recognition (Springer, 2009).
  3. M. Faundez-Zanuy, On-line Signature recognition base on VQ-DTW, Pattern Recognition, Vol 40, n. 3, pp. 981-932, 2007.
  4. H. Bunke, M. Roth, E.G. Scukat-Talamazzini, Off-line Cursive handwriting recognition hidden markov methods, Pattern Recognition, Vol 23, n. 9, pp. 1399-1413, 1995.
  5. S.Z. Li, L. Juwei, Face recognition using the nearest feature line method , IEEE Transaction on Neural Network, Vol 10, n. 2, pp. 439-443, 1999.
  6. G. Yongsheng, Face recognition using line edge map , IEEE Transaction on Pattern Analysis & machine intelligence, Vol 24, n. 6, pp. 764-779, 2002.
  7. J.K. Sing, D.K. Basu, M. Nasipuri, M. Kundu,, Face recognition using point summitry distance-based RBF network , Applied Soft Computing, Vol 7, n. 1, pp. 58-70, 2007.
  8. R. Gottmukkal, V.K. Asari, An improved face recognition technique based on modular PCA approach, Pattern Recognition Letter, Vol 25, n. 4, pp. 423-436, 2004.
  9. F. Zernik, Beugungstheorie des schneidenver-fahrens und seiner verbesserten form, der phasenkontrastmethode , Physica, Vol 1, n. 7-12, pp. 689-704, 1934.
  10. A. Saradha, S. Annadurai, A hybrid feature extraction approach for face recognition system , International Journal on Graphics, Vision and image processing, Vol 5, n. 5, pp. 23-30, 2005.
  11. J. Flusser, Moment Invariants in Image Analysis, Transaction on Engineering and Technology, Vol 11, pp. 196-201, 2006.
  12. J. Haddadnia, K. Faez, M. Ahmadi, A fuzzy hybrid learning algorithm for radial basis function neural network with application in human face recognition, Pattern Recognition , Vol 36, n. 5, pp. 1187-1202, 2003.
  13. F. Faghani, M.J. Nordin, S. Shojaeipour, Optimization of the performance face recognition using Adoboost-based, Intelligent Computing and Information Science (Springer-Velng Bertin Heidelberg, CCIS), Vol 36, pp. 359-365, 2011.
  14. J. Haddadnia, M. Ahmadi, N-Feature neural network human face recognition, Image and Vision Computing , Vol 22, n. 12, pp.
Index Terms

Computer Science
Information Sciences

Keywords

Component Analysis Neural Network RBF Face Recognition Zernike Moments.