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Reseach Article

Facial Face Recognition Method using Fourier Transform Filters Gabor and R_LDA

Published on None 2011 by Anissa Bouzalmat, Arsalane Zarghili, Jamal Kharroubi
Intelligent Systems and Data Processing
Foundation of Computer Science USA
ICISD - Number 1
None 2011
Authors: Anissa Bouzalmat, Arsalane Zarghili, Jamal Kharroubi
b7161f7c-78c9-4fb5-8bd7-336e4242066e

Anissa Bouzalmat, Arsalane Zarghili, Jamal Kharroubi . Facial Face Recognition Method using Fourier Transform Filters Gabor and R_LDA. Intelligent Systems and Data Processing. ICISD, 1 (None 2011), 18-24.

@article{
author = { Anissa Bouzalmat, Arsalane Zarghili, Jamal Kharroubi },
title = { Facial Face Recognition Method using Fourier Transform Filters Gabor and R_LDA },
journal = { Intelligent Systems and Data Processing },
issue_date = { None 2011 },
volume = { ICISD },
number = { 1 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 18-24 },
numpages = 7,
url = { /specialissues/icisd/number1/2313-23/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 Intelligent Systems and Data Processing
%A Anissa Bouzalmat
%A Arsalane Zarghili
%A Jamal Kharroubi
%T Facial Face Recognition Method using Fourier Transform Filters Gabor and R_LDA
%J Intelligent Systems and Data Processing
%@ 0975-8887
%V ICISD
%N 1
%P 18-24
%D 2011
%I International Journal of Computer Applications
Abstract

In this paper, we present a new approach for facial face recognition. The method is based on the Fourier transform of Gabor filters and the method of regularized linear discriminate analysis applied to facial features previously localized. The process of facial face recognition is based on two phases: location and recognition. The first phase determines the characteristic using the local properties of the face by the variation of gray level along the axis of the characteristic and the geometric model, and the second phase generates the feature vector by the convolution of the Fourier transform of 40 Gabor filters and face, followed by application of the method of regularized linear discriminate analysis on the vectors of characteristics. Experimental results obtained on sample of images from the XM2VTSDB database [1] have shown that the proposed algorithm gives satisfactory results in a precise manner.

References
  1. K Messer, J Matas, J Kittler, J Luettin, and G maitre, ” Xm2vtsdb: The extended m2vts database”, In Second International Conference of Audio and Video-based Biometric Person Authentication, March 1999 .
  2. K. Sandeep, A.N. Rajagopalan,”Human Face Detection in Cluttered Color Images Using Skin Color and Edge Information” ,ICVGIP Proceeding, 2002.
  3. Deng X , Chang C. H, Brandle E,”A new method for eye extraction from facial image”,Proc. 2nd IEEE international workshop on electronic dessign, test and applications (DELTA), 2 no.4:29–34, Perth, Australia ,2004.
  4. Maio D, Maltoni D,”Real-time face location on grayscale static images”,Pattern Recognition.33:1525–1539,2000.
  5. Tsekeridou S, Pitas I,”Facial feature extraction in frontal views using biometric analogies”,Proc.9th European Signal Processing Conference,1:315–318,Island of Rhodes, Greece,1998.
  6. Kapmann M, Zhang L,”Estimation of eye, eyebrow and nose features in videophone sequences”, International Workshop on Very Low Bitrate Video Coding (VLBV 98), pages 101–104, Urbana,USA,1998.
  7. Ko J-G, Kim K-N, Ramakrishma R. S,”Facial feature tracking for eye-head controlled human computer interface”,IEEE TENCON, Cheju, Korea,1999.
  8. Zhang Z., Lyons L., Schuster M, Akamatsu S ,”Comparison between geometrybased and gabor wavelets-based facial expression recognition using multilayer perceptron”,Proc. Automatic Face and Gesture Recognition, pages 454–459, Japan,1998.
  9. F.Abdat, C.Maaoui, A.Pruski, ”Suivi du gradient pour la localisation des caractéristiques faciales dans des images statiques”,Colloque GRETSI, 11-14, Troyes,2007.
  10. Frank Y. Shih, Chao-Fa Chuang, ”Automatic extraction of head and face boundaries and facial features”,Information Sciences 158 , 117–130,2004.
  11. J Essam Al Daoud, ”Enhancement of the Face Recognition Using a Modified Fourier-Gabor Filter”,Int. J. Advance. Soft Comput. Appl., Vol. 1, No. 2, 2009.
  12. B. Olshasen, D. Field, ”Emergence of simple-cell receptive field prop-erties by learning a sparse code for natural images”,Nature, 381:607609,1996.
  13. R. Rao, D. Ballard, ”An active vision architecture based on iconic representations”,Artificial Intelligence,78:461 505,1995.
  14. B. Schiele, J. Crowley, ”Recognition without correspondence using mul-tidimensional receptive field histograms”,International Journal on Com-puter Vision.36:3152,2000.
  15. Juwei Lu, K.N. Plataniotis, A.N. Venetsanopoulos,”Regularization studies of linear discriminant analysis in small sample size scenarios with application to Face recognition”,Bell Canada Multimedia LaboratoryThe Edward S. Rogers Sr. Department of Electrical and Computer EngineeringUniversity of Toronto, Toronto, M5S 3G4, ONTARIO, CANADA.
  16. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J, ”Eigenfaces versus fisherfaces:recognition using class specific linear projection”, IEEE Trans. Pattern Anal. Mach. Intell. Vol. 19 , No. 7 :p.711–720,1997.
Index Terms

Computer Science
Information Sciences

Keywords

Face detection Face recognition Facial features Fourier transform Localization features Gabor filter R_LDA