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
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Survey of Face Recognition Techniques

by Nilima B. Kachare, Vandana S. Inamdar
journal cover thumbnail
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 19
Year of Publication: 2010
Authors: Nilima B. Kachare, Vandana S. Inamdar
10.5120/408-604

Nilima B. Kachare, Vandana S. Inamdar . Survey of Face Recognition Techniques. International Journal of Computer Applications. 1, 19 ( February 2010), 29-33. DOI=10.5120/408-604

@article{ 10.5120/408-604,
author = { Nilima B. Kachare, Vandana S. Inamdar },
title = { Survey of Face Recognition Techniques },
journal = { International Journal of Computer Applications },
issue_date = { February 2010 },
volume = { 1 },
number = { 19 },
month = { February },
year = { 2010 },
issn = { 0975-8887 },
pages = { 29-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume1/number19/408-604/ },
doi = { 10.5120/408-604 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:46:52.361465+05:30
%A Nilima B. Kachare
%A Vandana S. Inamdar
%T Survey of Face Recognition Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 1
%N 19
%P 29-33
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Face recognition is a kind of automated biometric identification technique that recognizes an individual based on their facial features as essential elements of distinction. The research on face recognition has been actively going on in the recent years because face recognition spans numerous fields and disciplines such as access control, surveillance and security, credit-card verification, criminal identification and digital library. In this paper we discuss past research on biometric face feature extraction and recognition of static images. We will present implementation outline of these methods along with their comparative measures.

References
  1. K. Fukunaga, 1990. Introduction to Statistical Pattern Recognition. Academic Press, 1990.
  2. M. Turk and A. Pentland. 1991. Eigenfaces for recognition. Journal of Cognitive Science, pp.71-86.
  3. B.S. Manjunath, R. Chellappa, and C. von der Malsburg, 1992. A Feature Based Approach to Face Recognition. In Proc. of International Conf. on Computer Vision.
  4. F. Samaria, 1993. Face segmentation for identification using Hidden Markov Models. British Machine Vision Conference.
  5. F. Samaria and F. Fallside, 1993. Face identification and feature extraction using Hidden Markov Models. Image Processing: Theory and Applications.
  6. F. Samaria and F. Fallside, 1993. .Automated face identification using Hidden Markov Models," Proc. of the Int. Conference on Advanced Mechatronics,1993.
  7. A. Lanitis, C.J. Taylor, and T.F. Cootes, 1994. An automatic face identification system using flexible appearance models. British Machine Vision Conference, volume 1, pp. 65-74, BMVA Press.
  8. F. Samaria and S. Young, 1994. HMM based architecture for face identification. Image and Computer Vision, vol. 12, October 1994.
  9. R. Chellappa, C. Wilson, and S, Sirobey, 1995. Human and machine recognition of faces: A survey. Proceedings of IEEE, vol. 83, May 1995.
  10. K. Etemad and R. Chellapa, 1996. Face recognition using discriminant eigenvectors,Proc. of ICASSP.
  11. Belhumeur, P., Hespanha, J., Kriegman, D., 1996. Eigenfaces vs.Fisherfaces: Recognition Using Class Specific Linear Projection. Proceedings of the Fourth European Conference on Computer Vision, Vol. 1,Cambridge, UK, pp. 45-58, April 1996.
  12. T. S. Lee, "Image representation using 2-d Gabor wavelets," IEEE Trans. On Pattern Analysis Pattern Analysis and Machine Intelligence, vol. 18, no.10, October, 1996.
  13. K. Etemad and R. Chellappa, "Discriminant analysis for recognition of human face images," J. Opt. Soc. Am. A, vol. 14, pp.1724-1733, August 1997.
  14. W. Zhao, R. Chellappa, and A. Krishnaswamy. 1998. Discriminant analysis of principal components for face recognition. In Proceedings of the 3rd International Conference on Automatic Face and Gesture Recognition, 336-341.
  15. H. Moon and P. J. Phillips, 1998. Analysis of PCA-based face recognition algorithms. Empirical Evaluation Techniques in Computer Vision, IEEE Computer Society Press, Los Alamitos, CA.
  16. L. Wiskott, J. M. Fellous, N. Krüger and Christoph von der Malsburg, 1999. Face Recognition by Elastic Graph Matching. In Intelligent Biometric Techniques in fingerprint and Face Recognition, CRC Press, Chapter 11, pp. 355-396.
  17. B. Duc, S. Fisher, and J. Bigün, 1999. Face Authentication with Gabor Information on Deformable Graphs. IEEE Trans. On Image Proc., vol.8, no.4, pp.504-515.
  18. M. H. Yang, N. Ahuja, and D. Kriegman, 2001. A survey on face detection methods. IEEE Trans. On Pattern analysis and Machine Intelligance, to appear.
  19. Moon H., Phillips P.J., 2001. Computational and Performance Aspects of PCA-based face recognition Algorithms. Perception, Vol.30, pp. 303-321
  20. M. Turk, 2001. A Random Walk through Eigenspace. IEICE Trans. Inf. & Syst., Vol.E84-D, No. 12, December 2001, pp. 1586- 1595
  21. M.S. Bartlett, J.R. Movellan, and T.J. Sejnowski, 2002. Face recognition by independent component analysis. IEEE Trans. Neural Networks, vol. 13, no. 6, pp. 1450-1464.
  22. Juwei Lu, Kostantinos N. Plataniotis, and Anastasios N. Venetsanopoulos,2003. Face Recognition Using LDA-Based Algorithms. IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 14, NO. 1, JANUARY 2003
  23. Heseltine, T., Pears, N., Austin, J, 2003. Face Recognition: A Comparison of Appearance-Based Approaches. In Proc. VIIth Digital Image Computing: Techniques and Applications,10-12 Dec. 2003, Sydney
  24. PERLIBAKAS V., 2004. Face Recognition Using Principal Component Analysis and Wavelet Packet Decomposition. INFORMATICA, 2004, Vol. 15, No.
  25. Kresimir Delac, Mislav Grgic, Panos Liatsis, 2005. Appearance-based Statistical Methods for Face Recognition. 47th International Symposium ELMAR-2005, 08-10 June 2005.
  26. Kresimir Delac , Mislav Grgic and Sonja Grgic, 2005. Generalization Abilities of Appearance- Based Subspace Face Recognition Algorithms. In12th Int. Workshop on Systems, Signals & Image Processing, 22-24 September 2005, Chalkida, Greece.
  27. FERET latest evaluation results, http://www.itl.nist.gov/iad/humanid/feret/perf/eval.html
Index Terms

Computer Science
Information Sciences

Keywords

Automatic face recognition Appearance based recognition Principal component Feature extraction Maximum likelihood Hidden Markov Model Based method (HMM) statistical approaches template based approaches) and feature based methods eigenface fisherface Fisher's Linear Discriminant (FLD) Gabor Filter Gabor Coefficients

Powered by PhDFocusTM