We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Biometric Authentication with Face Recognition using Principal Component Analysis and Feature based Technique

by Onifade, F.w. Olufade, Adebayo, J. Kolawole
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 41 - Number 1
Year of Publication: 2012
Authors: Onifade, F.w. Olufade, Adebayo, J. Kolawole
10.5120/5504-7518

Onifade, F.w. Olufade, Adebayo, J. Kolawole . Biometric Authentication with Face Recognition using Principal Component Analysis and Feature based Technique. International Journal of Computer Applications. 41, 1 ( March 2012), 13-20. DOI=10.5120/5504-7518

@article{ 10.5120/5504-7518,
author = { Onifade, F.w. Olufade, Adebayo, J. Kolawole },
title = { Biometric Authentication with Face Recognition using Principal Component Analysis and Feature based Technique },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 41 },
number = { 1 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 13-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume41/number1/5504-7518/ },
doi = { 10.5120/5504-7518 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:28:28.334342+05:30
%A Onifade
%A F.w. Olufade
%A Adebayo
%A J. Kolawole
%T Biometric Authentication with Face Recognition using Principal Component Analysis and Feature based Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 41
%N 1
%P 13-20
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In today's fast paced networked world, the need to maintain the security of information or physical property is becoming both increasingly important and increasingly difficult. Recently, a ground breaking technology; biometrics, which is still a subject of growing research became available to allow verification of "true" individual identity. This is the focal point of our work where a face recognition system is implemented. We implemented an authentication system based on face recognition. We trained the images using principal component analysis and then combine with a feature based technique. For the feature based technique, we extract some key features including the red, green and blue colours of the eyes, the width and height of the eyes etc and ratios between them. We computed weights for each image based on these features and record the weights in the database for each subject in the database. We finally combine these feature weights with the weights computed from the principal component analysis and used it as the final weight to perform recognition. The system achieved a good recognition result.

References
  1. V. Matyas and Z. Riha, Biometric authentication- security and usabilityy, Faculty of informatics, masaryic university Brno, Czech Republic. 2008.
  2. V. Matyas and Z. Riha, "Towards reliable user authentication through biometrics". IEEE security and privacy journal, 2007.
  3. D. Kresmir, G. Mislav, and L. Panos, "Appearance Based statistical method for face recognition" in 47th international sysmposium, ELMAR 2005, Zedar, Croatia . June 2005.
  4. W. Zhao, R. Chellapa, and P. J Phillips, "Face Recognition: A literature survey ," in Technical Report, University of ,Maryland, 2000.
  5. F. Galton, "Personal identification and description 1,1 Nature, pp. 173-177,21 June1888
  6. W. W Bledsoe, "The model method in facial recognition", Panoramic Research Inc. Palo Alto, CA, Rep. PRI:15, (August 1966).
  7. M. A. Fischler, and R. A Elschlager, "The representation and matching of pictorial structures", IEEE Trans. on Computers, c-22. 1, (1973).
  8. A. L Yuille, D. S Cohen, and P. W. Hallinan, , "Feature extraction from faces using deformable templates", Proc. of CVPR, (1989).
  9. T. Kohonen, "Self-organization and associative memory", Berlin: Springer- Verlag, (1989).
  10. T. Kohonen, and P. Lehtio, "Storage and processing of information in distributed associative memory systems", (1981).
  11. M. Fleming and G. Cottrell, "Categorization of faces using unsupervised feature extraction", Proc. of IJCNN, Vol. 90(2), (1990).
  12. T. Kanade, "Picture processing system by computer complex and recognition of human faces", Dept. of Information Science, Kyoto University, (1973).
  13. J. Huang, "Detection strategies for face recognition using learning and evolution" PhD thesis, George Mason University. ,May 1998.
  14. M. Turk, and A. Pentland. , "Eigenfaces for recognition", Journal of Cognitive Neuroscience, Vol. 3, pp. 71-86, (1991).
  15. M. Kirby. , and L. Sirovich. , "Application of the Karhunen-Loeve procedure for the characterization of human faces", IEEE PAMI, Vol. 12, pp. 103-108, (1990).
  16. M. Kirby. , and L. Sirovich. , "Low-dimensional procedure for the characterization of human faces", J. Opt. Soc. Am. A, 4, 3, pp. 519-524, (1987).
  17. S. J. Lee, S. B. Yung, J. W. Kwon and S. H. Hong, "Face detection and recognition using PCA". Pg84-87. IEEE. TENCOM, 1999.
  18. J. L. Crowley and K. Schwerdt, "Robust tracking and compression for video communication". Pg 2-9. IEEE transaction on pattern recognition. 1999.
  19. B. Moghaddam, C. Naster and A. Pentland, "A Bayesian similarity measure for deformable image matching", Image and Vision computing, vol 19, may 2001.
  20. D. Murugan, S. Murugam,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 technology. Vol 2, no 1, feb 2010.
  21. B. Moghaddam. Principal manifolds and bayesian subspaces for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(6):780{788, June 2002.
  22. S. Cagnoni, A. Poggi, "A modular eigenspace approach to face recognition" Pg 490-495. IEEE Transaction on pattern recognition. 1999.
  23. S. Cagnoni, A. Poggi, "A modified modular eigenspace approach to face recognition" Pg 490-495. IEEE Transaction on pattern recognition. 1999.
Index Terms

Computer Science
Information Sciences

Keywords

Principal Component Analysis Feature Based Technique Biometric Authentication Threshold

Powered by PhDFocusTM