International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 4, No. 9, 8 Aug. 2012
Cover page and Table of Contents: PDF (size: 484KB)
Employing Fuzzy-Histogram Equalization to Combat Illumination Invariance in Face Recognition Systems
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Author(s)
Index Terms
Principal component analysis, Fuzzy Histogram Equalization, Biometric, Face recognition, Illumination Invariance
Abstract
With the recent surge in acceptance of face recognition systems, more and more work is needed to perfect the existing grey areas. A major concern is the issue of illumination intensities in the images used as probe and images trained in the database. This paper presents the adoption and use of fuzzy histogram equalization in combating illumination variations in face recognition systems. The face recognition algorithm used is Principal Component Analysis, PCA. Histogram equalization was enhanced using some fuzzy rules in order to get an efficient light normalization. The algorithms were implemented and tested exhaustively with and without the application of fuzzy histogram equalization to test our approach. A good and considerable result was achieved.
Cite This Paper
Adebayo Kolawole John, Onifade Onifade Williams, "Employing Fuzzy-Histogram Equalization to Combat Illumination Invariance in Face Recognition Systems", International Journal of Intelligent Systems and Applications(IJISA), vol.4, no.9, pp.54-60, 2012. DOI:10.5815/ijisa.2012.09.07
Reference
[1]W. Zhao, R. Chellapa, and P. J Phillips, “Face Recognition: A literature survey,” in Technical Report, University of Maryland, 2000.
[2]D. Kresmir, G. Mislav, and L. Panos, “Appearance Based statistical method for face recognition” in 47th international sysmposium, ELMAR 2005, Zedar, Croatia . June 2005.
[3]Liu Chang-Cun, Hu Sun-Bo, Yang Ji-Hong,et a1.A Method of Histogram Incomplete Equalization. In Journal of Shandong University (Engineering Science), vol.33,no.6, pp.661-664,2003.
[4]S Gonzalez R C, Woods R E. Digital Image Processing [M]. Beijing: Publishing House of Electronics Industry, 2002
[5]M.Pizer, E.RAmburm, J.D.Austin. Adaptive Histogram Equalization and its Variations[J]. Comput. Vision.Graphics Image Proeessing. vol.39, pp.355-368, 2007
[6]SHI De-Qin, LI Jun-Shan. A new Self-adaptive Enhancement Algorithm for the Low Light Level Night Vision Image[J].Electronic Optical and Control, vol.15, no.9,pp.18- 20,2008.
[7]LI Wen-Yong, GU Guo-Hua. A New Enhancement Algorithm for Infrared DIM-small Target Image[J]. Infrared. vol. 27, no. 3, pp.17-20, 2006.
[8]Ooi, Chen Hee; Kong, Nicholas Pik; Ibrahim, Haidi. Bi-histogram equalization with a plateau limit for digital image enhancement[J]. IEEE Transactions on Consumer Electronics, vol.55, no.4, pp.2072-2080, 2009.
[9]Yang Shubin, He Xi, Cao Heng and Cui Wanlong, Double-plateaus Histogram Enhancement Algorithm for Low-light-level Night Vision Image, Journal of Convergence Information Technology, Volume 6, Number 1. January 2011
[10]A.Laine,J.FanandW.Yang,”WaveletsforContrastEnhancementofDigitalMammography”,IEEE Eng.Med.Bio1.,vo1.14,pp.536-549,1995
[11]Guliato D., Rangayyan R.M., Carnielli W.A., Zuffo J.A., and Desautels J.E.L., " Segmetation of breast tumors in mammograms using fuzzy sets," Journal of Electronic Imaging, 12(3):369-378, (2003).
[12]Chealsea Robertson, "Adaptive fuzzy histogram equalization", 1996.
[13]EhsanNezhadarya,MohammadB.ShamsollahiandOmidSayadi,"FuzzyWavelet and Contourlet Based Contrast Enhancement", (1981).
[14]Onifade O.F.W and Adebayo K.J, “Biometric authentication with face recognition using principal component analysis and a feature based technique” in IJCA, Vol 41, No 1 – March 2012.
[15]M. Turk, and A. Pentland., "Eigenfaces for recognition", Journal of Cognitive Neuroscience, Vol. 3, pp. 71-86, (1991).
[16]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).
[17]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).
[18]Zadeh, Lotfi: “Fuzzy Sets” Information and Control. 8(3), pp.338-35Cited by [Klir 1995, 1997], [Bonissone 1997])
[19]Adebayo Kolawole John: “Combating terrorism with biometric authentication using face recognition”. In proc of 10th international conference of the Nigeria Computer Society, 2011. Vol 10 pg 55-65. Available online at www.ncs.org.
[20]Adebayo Kolawole John and Onifade Olufade: “Framework for a Dynamic Grid-Based Surveillance Face Recognition System”. In Africa journal of computing and ICT (IEEE Nigerian Section), pp. 1 – 10, Vol. 4 N0. 1, June, 2011.