Several Face Images Recognition in Uncontrolled Illumination Variation Images
DOI:
https://doi.org/10.51983/ajcst-2012.1.1.1686Keywords:
Face recognition, Face recognition grand challenge, Feature extraction, preprocessing, score fusionAbstract
This paper to study and present a face recognition system under uncontrolled illumination images. It consists of pre-processing method, a hybrid Fourier feature extraction and a score fusion. In the pre-processing stage a face image is transformed in to an illumination-insensitive image, called an “integral normalized gradient image”. The hybrids Fourier feature are extracted from different Fourier domains in different frequency bandwidth. In this paper the multiple Face Models are generated by plural normalized face images that have different eye distances. Finally to combine the scores from multiple complementary classifiers, a log likelihood ratio-based score fusion scheme is applied. This system using the Face recognition Grand Challenge (FRGC) experimental protocols is evaluated. it shows the average verification rate on 2-D face images under various environmental variations such as illumination changes, expression changes, and time elapses.
References
D.M. Blackburn, M. Bone, and P. J. Phillips, “Facial Recognition Vendor test 2000 evaluation report,” IEEE Transaction Pattern Analysis and Machine Intelligence., Vol. 22, No.10, PP.1090-1104, Oct.2000.
P.Phillips, P. Grother, R. Micheals, D. Blackburn, E. Tabassi, and M.Bone, “Face recognition vendor Test 2002:evaluation report,” 2003[online]. Available: http://www.frvt.org/
P.J.Phillips, P.J.Flynn, T.Scruggs, K.Bowyer, J.Chang, K.Hoffmann, J.Marques, J.Min and W. Worek,”Overview of the face recognition grand challenge” in proceedings of IEEE.Computer Vision and Pattern Recognition., Jun.2005, Vol. 1, PP.947-954.
P.N.Belhumer and D.J. Kriegman “What is the set of images of an object under all possible lighting conditions?” in proceedings of IEEE conference and Computer Vision Pattern recognition., Vol.5, No.13,PP.523-527,Jun.1996
A.shashua and T.Riklin-Raviv,” The Quotient image: Class-Based re-rendering and recognition with illuminations”, IEEE Computer Vison and Pattern recognition., Vol.25,PP.270-277,Feb.2001.
H.Wang et.al,”Generalized Quotient image” in proceedings of IEEE.Computer Vision Pattern Recognition.,Vol.2, No.13,PP.498-505. Jun.2004.
D.J.Jobson, Z.Rahman, and G.A. Woodell, “properties and Performance of a center/surround Retenix”, IEEE Transaction Image Processing., Vol.6, No.3, PP. 451-462, Mar.1997.
B.V.Kumar, M.Savvides, K.Venkataramani, and X.Xie, “Spatial frequency domain image processing for biometric recognition”, in Proceeding IEEE.Intl. Conference. Image Processing., Vol. 1, PP.53-56,Jun.2002.
J.Yang and C.Liu, “Color image discriminant models and algorithms for face recognition”, IEEE Trans.Neural Networks., Vol. 19, No. 12, PP. 88-98, Dec.2008.
X.Tan and B.Triggs, “Fusing gabor and LBP feature set for kernel-based face recognition”, in Proc. IEEE int. Workshop Analy.Model.Face Gestures, PP.235-249, July.2007.
.Y.Su, S.Shan, X.Chen and W.Gao, “Hierarchial Ensemble of Global and local classifiers for face recognition”, IEEE Trans. Image processing., Vol. 18, No.8, PP.85-96, Aug.2009.
T. Kim, H. Kim, W. Hwang, and J. Kittler, “Component-based LDA face description for image retrival and MPEG -7 Standardisation”, Image VIs. Computer., Vol. 23, No. 7. PP. 48-62, July. 2005.
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