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Face Recognition by Radial Basis Function Network (RBFN)

by Mrinal Kanti Dhar, Quazi M. Hasibul Haque, Md. Tanjimuddin
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 78 - Number 3
Year of Publication: 2013
Authors: Mrinal Kanti Dhar, Quazi M. Hasibul Haque, Md. Tanjimuddin
10.5120/13470-1143

Mrinal Kanti Dhar, Quazi M. Hasibul Haque, Md. Tanjimuddin . Face Recognition by Radial Basis Function Network (RBFN). International Journal of Computer Applications. 78, 3 ( September 2013), 21-26. DOI=10.5120/13470-1143

@article{ 10.5120/13470-1143,
author = { Mrinal Kanti Dhar, Quazi M. Hasibul Haque, Md. Tanjimuddin },
title = { Face Recognition by Radial Basis Function Network (RBFN) },
journal = { International Journal of Computer Applications },
issue_date = { September 2013 },
volume = { 78 },
number = { 3 },
month = { September },
year = { 2013 },
issn = { 0975-8887 },
pages = { 21-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume78/number3/13470-1143/ },
doi = { 10.5120/13470-1143 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:50:40.173995+05:30
%A Mrinal Kanti Dhar
%A Quazi M. Hasibul Haque
%A Md. Tanjimuddin
%T Face Recognition by Radial Basis Function Network (RBFN)
%J International Journal of Computer Applications
%@ 0975-8887
%V 78
%N 3
%P 21-26
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Face recognition technology using Radial Basis Function Network (RBFN) is an attractive solution for the researchers who are working on the field of machine recognition, pattern recognition and computer vision. The key challenge in the face recognition technology is to provide high recognition rate. In this paper, an efficient method has been presented for face recognition using principal component analysis and radial basis function. More specifically, principal component analysis has been used for feature extraction and radial basis function network has been used as a classifier to classify data as well as for recognition process.

References
  1. Meng Joo Er, Shiqian Wu, Juwei Lu, Hock Lye Toh, "Face Recognition With Radial Basis Function (RBF) Neural Networks", IEEE transactions on neural networks, vol. 13, no. 3, may 2002.
  2. V. Radha, N. Nallammal "Neural Network Based Face Recognition Using RBFN Classifier", Proceedings of the World Congress on Engineering and Computer Science 2011 Vol I WCECS 2011, October 19-21, 2011, San Francisco, USA.
  3. L. Wang , X. Wang and J. Feng "On image matrix based feature extraction algorithms", IEEE Trans. Syst. , Man, Cybern. B, Cybern. , vol. 36, no. 1, pp. 194 -197 2006
  4. Suganthy, M. and P. Ramamoorthy, "Principal Component Analysis Based Feature Extraction, Morphological Edge Detection and Localization for Fast Iris Recognition", Journal of Computer Science 8 (9), pp. 1428-1433, 2012
  5. Wang, Y. , Jiar, Y. , Hu, C. , & Turk, M "Face recognition based on kernel radial basis function networks". Asian Conference on Computer Vision, Korea. (2004, January 27-30).
  6. Lindsay I Smith "A tutorial on Principal Components Analysis" February 26, 2002.
  7. Powell, M. J. D, "Radial basis functions for multivariable interpolation:A review" In Algorithms for Approximation, J. C. Mason and M. G. Cox, Eds. Oxford University Press, Oxford, UK, 1987, pp. 143–167
  8. L. N. M. Tawfiq ,Q. H. Eqhaar, "ON RADIAL BASIS FUNCTION NEURAL NETWORKS"Journal of al-qadisiyah for pure science(quarterly). Vol-12, pages-12-18.
  9. Babu, R. V. Suresh, S. Makur, "A. ROBUST OBJECT TRACKING WITH RADIAL BASIS FUNCTION NETWORKS" IEEE international conference on Acoustics, Speech and Signal processing, 2007. Volume: 1, Page(s): I-937 - I-940.
  10. Aleix M. MartõÂnez, Avinash C. Kak, "A. ROBUST OBJECT TRACKING WITH RADIAL BASIS FUNCTION NETWORKS" IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 228-233, February 2001.
  11. Tiantian Xie, Hao Yu and Bogdan Wilamowski, "Comparison between Traditional Neural Networks and Radial Basis Function Networks"Industrial Electronics(ISIE), 2011 IEEE International Symposium on, pp. 1194-1199, Date of conference 27-30 June, 2011
  12. M. Turk, A. Pentland, "Eigen faces for Recognition", Journal of Cognitive Neuroscience, Vol. 3, No. 1, 1991,pp. 71-86
Index Terms

Computer Science
Information Sciences

Keywords

Face recognition Principal component analysis Artificial neural network Radial basis function network.

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