CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

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

Comparative Analysis of Conventional, Real and Complex Wavelet Transforms for Face Recognition

by Ankit A. Bhurane, Sanjay N. Talbar, Preeti N. Gophane
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 39 - Number 6
Year of Publication: 2012
Authors: Ankit A. Bhurane, Sanjay N. Talbar, Preeti N. Gophane
10.5120/4822-7073

Ankit A. Bhurane, Sanjay N. Talbar, Preeti N. Gophane . Comparative Analysis of Conventional, Real and Complex Wavelet Transforms for Face Recognition. International Journal of Computer Applications. 39, 6 ( February 2012), 6-12. DOI=10.5120/4822-7073

@article{ 10.5120/4822-7073,
author = { Ankit A. Bhurane, Sanjay N. Talbar, Preeti N. Gophane },
title = { Comparative Analysis of Conventional, Real and Complex Wavelet Transforms for Face Recognition },
journal = { International Journal of Computer Applications },
issue_date = { February 2012 },
volume = { 39 },
number = { 6 },
month = { February },
year = { 2012 },
issn = { 0975-8887 },
pages = { 6-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume39/number6/4822-7073/ },
doi = { 10.5120/4822-7073 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:26:14.150126+05:30
%A Ankit A. Bhurane
%A Sanjay N. Talbar
%A Preeti N. Gophane
%T Comparative Analysis of Conventional, Real and Complex Wavelet Transforms for Face Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 39
%N 6
%P 6-12
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Face recognition has been studied for many years in the context of biometrics and is one of the most successful applications of image analysis and understanding. Various methods, approaches and algorithms for recognition of human faces were proposed. In this paper, independent, comparative study of conventional discrete wavelet transform (DWT), real dual-tree discrete wavelet transform (R-DT-DWT), and complex dual-tree discrete wavelet transform (C-DT-DWT) based features for face recognition is carried out. In 2005, Delac et al. [26] presented an independent comparative study of PCA, ICA, and LDA on the FERET data set where it was concluded that no particular distance–metric combination is the best. In this paper we intend to bring further conclusions. Unlike the contribution by Delac et al., our conclusions are in context of DWT, R-DT-DWT, and C-DT-DWT. Moreover, these approaches are tested on nine different databases at different levels and under three different distance metrics, which allowed us to compare their performance independently. Our simulation results show that no particular distance–metric combination is the best across all standard benchmark face databases. However, the overall performance for city block distance measure was found to be better as compared to the Euclidean and cosine distance. Also, the performance for R-DT-DWT and C-DT-DWT based features were found equivalently efficient in many cases. So taking redundancy into consideration, it may be suggested to opt for R-DT-DWT for face efficient recognition.

References
  1. W. Zhao, R. Chellappa, A. Rosenfeld, P. J. Phillips, Face Recognition: A Literature Survey, ACM Computing Surveys, 35(4): 399-458, 2003.
  2. N. Kingsbury. The dual-tree complex wavelet transform: A new technique for shift invariance and directional filters. IEEE Digital Signal Processing Workshop, DSP 98, paper no. 86, August 1998.
  3. I. Selesnick, R. Baraniuk, and N. Kingsbury. The dual-tree complex wavelet transform. IEEE Signal Process. Mag., 22(6):123–151, Nov. 2005.
  4. A. F. Abdelnour and I. W. Selesnick. Nearly symmetric orthogonal wavelet bases. In Proceedings of IEEE International Conference on Acoustic, Speech, Signal Processing (ICASSP), May 2001.
  5. A. A. Bhurane. “Face Recognition using Dual-Tree Discrete Wavelet Transforms”. M.Tech Thesis, S.G.G.S. Institute of Engineering and Technology, Nanded, Maharashtra, India, July 2011.
  6. The color FERET database, USA. Website. http://face.nist.gov/colorferet/.
  7. AT & T: The database of faces (formerly: The ORL database of faces). Website. http://www.cl.cam.ac.uk/Research/DTG/attarchive/facedatabase.html.
  8. The Yale face database. Website. http://cvc.yale.edu/projects/yalefaces/yalefaces.html.
  9. The Japanese Female Facial Expression (JAFFE) database. Website. http://www.kasrl.org/jaffe.html.
  10. The University of Bern face database. Website. http://www.ph.tn.tudelft.nl/PRInfo/data/msg00010.html.
  11. The IITK face database. Website. http://vis-www.cs.umass.edu/~vidit/IndianFaceDatabase/.
  12. The CVSR Grimace database. Website. http://cswww.essex.ac.uk/mv/allfaces/index.html.
  13. The Georgia Tech. face database. Website. http://www.anefian.com/research/GTdb_crop.zip.
  14. The SGGS face database. Website. http://sggs.ac.in.
  15. L. Nanni and D. Maio, “Weighted sub-Gabor for Face Recognition,” Pattern Recognition Letters, vol. 28, no. 4, pp. 487–492, 2007.
  16. W.-P. Choi, S.-H. Tse, K.-W. Wong, and K.-M. Lam, “Simplified Gabor Wavelets for Human Face Recognition,” Pattern Recognition Letters, vol. 41, no. 3, pp. 1186–1199, 2008.
  17. D.-H. Liu, K.-M. Lam, and L.-S. Shen, “Optimal Sampling of Gabor Features for Face Recognition,” Pattern Recognition Letters, vol. 25, no. 2, pp. 267–276, 2004.
  18. E P Simoncelli, W T Freeman, E H Adelson and D J Heeger, “Shiftable Multiscale Transforms”, IEEE Transactions on Information Theory, 38(2), pp 587-607, March 1992.
  19. N G Kingsbury, “The Dual-Tree Complex Wavelet Transform: A New Technique for Shift Invariance and Directional Filters”, Proc. 8th IEEE DSP Workshop, Bryce Canyon, Aug 1998.
  20. N G Kingsbury: “The Dual-Tree Complex Wavelet Transform: A New Efficient Tool for Image Restoration and Enhancement”, Proc. EUSIPCO 98, Rhodes, Sept 1998.
  21. H. E. Sankaran, A. P. Gotchev, K. O. Egiazarian, and J. T. Astola, “Complex Wavelets versus Gabor Wavelets for Facial Feature Extraction: A Comparative Study,” in Image Processing: Algorithms and Systems IV, vol. 5672 of Proceedings of SPIE, pp. 407–415, San Jose, Calif, USA, January 2005.
  22. T. Celik, H. Ozkaramanli, and H. Demirel, “Facial Feature Extraction using Complex Dual-Tree Wavelet Transform,” Computer Vision and Image Understanding, vol. 111, no. 2, pp. 229–246, 2008.
  23. A. Eleyan, H. Ozkaramanli, and H. Demirel, “Complex Wavelet Transform-Based Face Recognition,” EURASIP Journal on Advances in Signal Processing, 2008, Article ID 185281.
  24. Y. Sun and M. Du, “DT-CWT Feature Based Classification using Orthogonal Neighborhood Preserving Projections for Face Recognition,” in Proceedings of the International Conference on Computational Intelligence and Security (CIS ’06), pp. 719– 724, Guangzhou, China, November 2006.
  25. Y. Sun, “DT-CWT Feature based Face Recognition using Supervised Kernel ONPP,” in Proceedings of the International Conference on Computational Intelligence and Security Workshops (CISW ’07), pp. 312–315, Harbin, China, December 2007.
  26. Delac K., Grgic M., and Grgic S. Independent comparative study of PCA, ICA, and LDA on the FERET data set. Wiley Periodicals, 15:252–260, 2005.
  27. Bhurane A.A. and Talbar S.N. “Vision-based authenticated robotic control using face and hand gesture recognition,” International Conference on on Electronics Computer Technology (ICECT 2011), vol V-1, pp 64–68. ICECT, IEEE, April 2011.
Index Terms

Computer Science
Information Sciences

Keywords

Real Dual-tree Discrete Wavelet Transform (R-DT-DWT) Complex dual-tree discrete wavelet transform (C-DT-DWT) Face Recognition Technology (FERET)

Powered by PhDFocusTM