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Face Illumination Normalization based on Illumination-Separated Face Identity Texture Subspace  

Choi, Jong-Keun (School of Electronic Engineering, Soongsil University)
Chung, Sun-Tae (School of Electronic Engineering, Soongsil University)
Cho, Seong-Won (Department of Electronic and Electrical Engineering, Hongik University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.47, no.1, 2010 , pp. 25-34 More about this Journal
Abstract
Robust face recognition under various illumination environments is difficult to achieve. For robust face recognition with respect to illumination variations, illumination normalization of face images is usually applied as a preprocessing step. Most of previously proposed illumination normalization methods cannot handle cast shadows in face images effectively. In this paper, We propose a new face illumination normalization method based on the illumination-separated face identity texture subspace. Since the face identity texture subspace is constructed so as to be separated from the effects of illumination variations, the projection of face images into the subspace produces a good illumination-normalized face images. Through experiments, it is shown that the proposed face illumination normalization method can effectively eliminate cast shadows as well as attached shadows and achieves a good face illumination normalization.
Keywords
얼굴 인식;조명 정규화;얼굴 텍스쳐;얼굴 텍스쳐 공간 모델링;
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1 R. Gross and V. Brajovic, "An image preprocessing algorithm for illumination invariant face recognition," In Audio-and Video-Based Biometric Person Authentication, Vol. 2688, pp. 10-18, June 2003.   DOI
2 R. Epstein, P. Hallinan, and A. Yuille, "5+/-2 eigenimages suffice: An empirical investigation of low-dimensional lighting models," Proc. Workshop Physics-Based Modeling in Computer Vision, 1995.
3 F. Kahraman and et al., "An Active Illumination and Appearance (AIA) Model for Face Alignment," Proc. of the CVPR 2007, IEEE Computer Society Workshop on Biometrics, 2007.
4 J. C. Gower, "Generalized Procrustes analysis," Psychometrika, Vol. 40, pp. 33-51, 1975.   DOI
5 M. Turk and A. Pentland, "Eigenfaces for recognition," Journal of Cognitive Neuroscience 3 (1), pp. 71–86, 1991.   DOI   ScienceOn
6 CMU PIE Face database, http://www.ri.cmu.edu/research_project_detail.html?project_id=418&menu_id=261
7 R. Basri and D.W. Jacobs, "Lambertian reflec -tances and linear subspaces," IEEE Int. Conf. on Computer Vision, pp. 383–390, 2001.
8 E. H. Land and J. J. McCann, "Lightness and retinex theory," Journal of the Optical Society of America, pp. 61:1-11, 1971.   DOI
9 T. F. Cootes, D. J. Edwards, and S. J. Taylor, "Active Appearance Models," IEEE Trans. Pattern Anal. Mach. Intell., Vol. 23, no. 6, pp. 681–685, Jun. 2001.   DOI   ScienceOn
10 B. Horn, "Robot Vision," MIT Press, 1986.
11 P. N. Belhumeur and D. J. Kriegman, "What is the set of images of an object under all possible lighting conditions?," IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'96), pp. 270-280, 1996.
12 S. Z. Li and A. K. Jain, "Handbook of Face Recognition," 2004.
13 D.T. Lee, and B. J. Schachter, "Two Algorithms for Constructing a Delaunay Triangulation," Int. J. Computer Information Sci. Vol. 9, pp. 219-242, 1980.   DOI
14 I. T. Jollie, "Principal Component Analysis," Springer - Verlag, New York, 1986.