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

Face Recognition using Eigenvector and Principle Component Analysis

by Dulal Chakraborty, Sanjit Kumar Saha, Md. Al-amin Bhuiyan
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 50 - Number 10
Year of Publication: 2012
Authors: Dulal Chakraborty, Sanjit Kumar Saha, Md. Al-amin Bhuiyan
10.5120/7811-0947

Dulal Chakraborty, Sanjit Kumar Saha, Md. Al-amin Bhuiyan . Face Recognition using Eigenvector and Principle Component Analysis. International Journal of Computer Applications. 50, 10 ( July 2012), 42-49. DOI=10.5120/7811-0947

@article{ 10.5120/7811-0947,
author = { Dulal Chakraborty, Sanjit Kumar Saha, Md. Al-amin Bhuiyan },
title = { Face Recognition using Eigenvector and Principle Component Analysis },
journal = { International Journal of Computer Applications },
issue_date = { July 2012 },
volume = { 50 },
number = { 10 },
month = { July },
year = { 2012 },
issn = { 0975-8887 },
pages = { 42-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume50/number10/7811-0947/ },
doi = { 10.5120/7811-0947 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:49:08.803930+05:30
%A Dulal Chakraborty
%A Sanjit Kumar Saha
%A Md. Al-amin Bhuiyan
%T Face Recognition using Eigenvector and Principle Component Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 50
%N 10
%P 42-49
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Face recognition is an important and challenging field in computer vision. This research present a system that is able to recognize a person's face by comparing facial structure to that of a known person which is achieved by using frontal view facing photographs of individuals to render a two-dimensional representation of a human head. Various symmetrization techniques are used for preprocessing the image in order to handle bad illumination and face alignment problem. We used Eigenface approach for face recognition. Eigenfaces are eigenvectors of covariance matrix, representing given image space. Any new face image can then be represented as a linear combination of these Eigenfaces. This makes it easier to match any two given images and thus face recognition process. The implemented eigenface-based technique classified the faces 95% correctly.

References
  1. F. Galton, "Personal identification and description 1,1 Nature, pp. 173-177,21 June1988
  2. Sir Francis Galton, Personal identification and description-II", Nature 201-203, 28 June 1988
  3. Goldstein, A. J. , Harmon, L. D. , and Lesk, A. B. , Identification of human faces", Proc. IEEE 59, pp. 748-760, (1971).
  4. Haig, N. K. , "How faces differ - a new comparative technique", Perception 14, pp. 601-615, (1985).
  5. Rhodes, G. , "Looking at faces: First-order and second order features as determinants of facial appearance", Perception 17, pp. 43-63, (1988).
  6. Kirby, M. , and Sirovich, L. , "Application of the Karhunen-Loeve procedure for the characterization of human faces", IEEE PAMI, Vol. 12, pp. 103-108, (1990).
  7. Sirovich, L. , and Kirby, M. , "Low-dimensional procedure for the characterization of human faces", J. Opt. Soc. Am. A, 4, 3, pp. 519-524, (1987).
  8. Terzopoulos, D. , and Waters, K. , "Analysis of facial images using physical and anatomical models", Proc. 3rd Int. Conf. on Computer Vision, pp. 727-732, (1990).
  9. Manjunath, B. S. , Chellappa, R. , and Malsburg, C. , "A feature based approach to face recognition", Trans. of IEEE, pp. 373-378, (1992).
  10. Harmon, L. D. , and Hunt, W. F. , "Automatic recognition of human face profiles", Computer Graphics and Image Processing, Vol. 6, pp. 135-156, (1977).
  11. Harmon, L. D. , Khan, M. K. , Lasch, R. , and Raming, P. F. , "Machine identification of human faces", Pattern Recognition, Vol. 13(2), pp. 97-110, (1981).
  12. Kaufman, G. J. , and Breeding, K. J, "The automatic recognition of human faces from profile silhouettes", IEEE Trans. Syst. Man Cybern . , Vol. 6, pp. 113-120, (1976).
  13. Wu, C. J. , and Huang, J. S. , "Human face profile recognition by computer", Pattern Recognition, Vol. 23(3/4), pp. 255-259, (1990).
  14. Kerin, M. A. , and Stonham, T. J. , "Face recognition using a digital neural network with self-organizing capabilities", Proc. 10th Int. Conf. On Pattern Recognition, pp. 738-741, (1990).
  15. Nakamura, O. , Mathur, S. , and Minami, T. , "Identification of human faces based on isodensity maps", Pattern Recognition, Vol. 24(3), pp. 263-272, (1991).
  16. Turk, M. , and Pentland, A. , "Eigenfaces for recognition", Journal of Cognitive Neuroscience, Vol. 3, pp. 71-86, (1991).
  17. Yuille, A. L. , Cohen, D. S. , and Hallinan, P. W. , "Feature extraction from faces using deformable templates", Proc. of CVPR, (1989).
Index Terms

Computer Science
Information Sciences

Keywords

Principle component analysis eigenvector eigenvalue eigenface faces recognition

Powered by PhDFocusTM