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Reseach Article

Efficient Face Recognition System using Artificial Neural Network

by S.adebayo Daramola, O. Sandra Odeghe
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 41 - Number 21
Year of Publication: 2012
Authors: S.adebayo Daramola, O. Sandra Odeghe
10.5120/5823-8042

S.adebayo Daramola, O. Sandra Odeghe . Efficient Face Recognition System using Artificial Neural Network. International Journal of Computer Applications. 41, 21 ( March 2012), 12-15. DOI=10.5120/5823-8042

@article{ 10.5120/5823-8042,
author = { S.adebayo Daramola, O. Sandra Odeghe },
title = { Efficient Face Recognition System using Artificial Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 41 },
number = { 21 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 12-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume41/number21/5823-8042/ },
doi = { 10.5120/5823-8042 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:30:16.133196+05:30
%A S.adebayo Daramola
%A O. Sandra Odeghe
%T Efficient Face Recognition System using Artificial Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 41
%N 21
%P 12-15
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Effective facial feature is needed to build a robust face recognition system capable of suppress the effect of illumination and pose variation. In this paper, a robust face recognition system is proposed. In the proposed system, two level haar wavelet transform is used to decompose frontal face image into seven sub-image bands. Thereafter eigenface feature is extracted from these bands. The feature is used as input to the classification algorithm based on Back Propagation Neural Network (BPNN). The proposed system has been tested using 150 frontal face samples with illumination and pose variation. The results obtained are very encouraging.

References
  1. A. M Patil, S. R Kolhe and P. M Patil, "2D Face Recognition Techniques: A Survey", International Journal of Machine Intelligence, Volume 2, Issue 1, 2010, pp-74-83.
  2. M. Agarwal, N. Jain, M. Kumar and H. Agrawal, "Face Recognition Using Eigen Faces and Artificial Neural Network", International Journal of Computer Theory and Engineering, Vol. 2, No. 4, August, 2010, pp624-629.
  3. P. Latha, Dr. . L. Ganesan and Dr. . S. Annadurai, "Face Recognition using Neural Networks", Signal Processing: An International Journal (SPIJ) Volume (3): Issue (5) pp153-160.
  4. S. Kumar Paul , M. Shorif Uddin and S. Bouakaz, "Extraction of Facial Feature Points Using Cumulative Histogram", IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 1, No 3, January 2012 pp44-51.
  5. R. 1 Dewi Agushinta, Adang Suhendra, Sarifuddin Madenda, H. S Suryadi, "Face Component Extraction Using Segmentation Method on Face Recognition System", Journal of Emerging Trends in Computing and Information Sciences, Volume 2 No. 2, pp67-72.
  6. Dr. H. B. Kekre1, S. D. Thepade, T. Chopra, "Face and Gender Recognition Using Principal Component Analysis", International Journal on Computer Science and Engineering, Vol. 02, No. 04, 2010, pp959-964.
  7. K. Ramirez-Gutierrez, D. Cruz-Perez, J. Olivares-Mercado, M. Nakano-Miyatake, and H. Perez-Meana, "A Face Recognition Algorithm using Eigenphases and Histogram Equalization", International Journal of Computers Issue 1, Volume 5, 2011 pp34-41.
  8. M. A. Kashem, M. N. Akhter, S. Ahmed, and M. M. Alam, "Face Recognition System Based on Principal Component Analysis (PCA) with Back Propagation Neural Networks (BPNN)", Canadian Journal on Image Processing and Computer Vision, Vol. 2, No. 4, 2011.
  9. A. Bastani and E. F. Behbahani, "An Efficient Feature Extraction Method with Orthogonal Moments and Wavelet Transform for Human Face Recognition System", European Journal of Scientific Research, Vol. 52 No. 3 (2011), pp. 313-320.
  10. K. Meethongjan, M. Dzulkifli, A. Rehman and T. Saba, "Face Recognition based on Fusion of Voronoi Diagram Automatic Facial and Wavelet Moment Invariants", International Journal of Video & Image Processing and Network Security IJVIPNS (10) 4, 2010.
  11. D. Murugan, Dr. S Arumugam, K. Rajalakshmi and T. I. Manish, "Performance evaluation of face recognition using Gabor filter, log Gabor filter and discrete wavelet transform", International Journal of Computer Science and Information (IJSCI) (2)1, 2010.
  12. G. Aguilar-Torres, K. Toscano-Medina, G. Sanchez-Perez, M. Nakano-Miyatake, and H. Perez-Meana, "Eigenface-Gabor Algorithm for Features Extraction in Face Recognition", International Journal of Computers, Issue 1, Volume 3, 2009.
  13. R. M Rahman, Anirban Das M Russel and Md. Shazzad Maruf, "Face Recognition for Single and Different Facial Expressions", Global Journal of Computer Science and Technology, Vol. 10 Issue 9, 2010, pp16-21.
  14. S. E Umbaugh "Computer Imaging: Digital Image Analysis processing", A CRC press book.
  15. Raju, U. S. N. , A. Srikrishna, V. Vijaya Kumar and A. Suresh, "Extraction of Skeleton Primitives on Wavelets", Journal of Theoretical and Applied Information Technology, pp1065 – 1074, 2008.
  16. R. C. Gonzalez and R. E. Woods, "Digital Image processing", 3rd Edition, Pearson International Edition.
Index Terms

Computer Science
Information Sciences

Keywords

Eigenface Haar Wavelet Transform And Neural Network