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Shadow determination and compensation for face recognition

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Abstract

Illumination variation that occurs on face images will significantly influence the performance of the recognition. Based on the low-dimensional intrinsic of face images, we design a novel two-step shadow compensation method for face recognition. Three indexes are proposed and employed to distinguish the shaded-images. Then, we compensate the shadows adaptively by using a modified Robust PCA result. Experimental results on Yale database and Yale B database demonstrate that the proposed approach can improve the recognition rate. Showing our method is suitable for face recognition with illumination variations.

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References

  1. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71-86

    Article  Google Scholar 

  2. Martinez AM, Kak AC (2001) PCA versus LDA. IEEE Trans Pattern Anal Mach Intell 23(2):228-233

    Article  Google Scholar 

  3. Li M, Yuan B (2005) 2D-LDA: a statistical linear discriminant analysis for image matrix. Pattern Recogn Lett 26(5):527-532

    Article  Google Scholar 

  4. Gong X, Li XX, Feng L, Xia R (2012) A robust framework for face contour detection from clutter background. Int J Mach Learn Cybern 3(2):111-118

    Article  Google Scholar 

  5. Poon B, Amin MA, Yan H (2011) Performance evaluation and comparison of PCA based human face recognition methods for distorted images. Int J Mach Learn Cybern 2(4):245-259

    Article  Google Scholar 

  6. Xu X, Liu W, Venkatesh S (2012) An innovative face image enhancement based on principle component analysis. Int J Mach Learn Cybern 3(4):259-267

    Article  Google Scholar 

  7. Xie X, Lam KM (2005) Face recognition under varying illumination based on a 2D face shape model. Pattern Recogn 38(2):221-230

    Article  Google Scholar 

  8. Struc V, Zibert J, Pavesic N (2009) Histogram remapping as a preprocessing step for robust face recognition. WSEAS Trans Inf Sci Applicat 6(3):520-529

    Google Scholar 

  9. Chen W, Joo Er M, Wu S (2006) Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain. IEEE Trans SMC-B 36(2):458-466

    Google Scholar 

  10. Hsieh P, Tung P (2010) Shadow compensation based on facial symmetry and image average for robust face recognition. Neurocomputing 73:2708-2717

    Article  Google Scholar 

  11. Jobson DJ, Rahman Z, Woodell GA (1997) Properties and performance of a center/surround retinex. IEEE Trans Image Process 6(3):451-462

    Article  Google Scholar 

  12. Jobson DJ, Rahman Z, Woodell GA (1997) A multi-scale retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans Image Process 6(7):965-976

    Article  Google Scholar 

  13. Lee KC, Ho J, Kriegman D (2001) Nine points of light: acquiring subspaces for face recognition under variable lighting. IEEE Conf Comput Soc Comput Vis Pattern Recogn 1:519-526

    Google Scholar 

  14. Gao Y, Leung MKH (2002) Face recognition using line edge map. IEEE Trans Patt Anal Mach Intell 24(6):764-779

    Article  Google Scholar 

  15. Yu HL, Ren HR, Yan K.: (2010) Contourlet transform based algorithm of shadow compensation for face recognition. IEEE Conf Comput Soc Image Analysis Signal Process (IASP) 671-674

  16. Son TT, Mita S (2005) Face recognition under variable lighting using the meanfield method and the gray-level pyramid. IEEE Int Conf Syst Man Cybern 3:2107-2113

    Google Scholar 

  17. An GY, Wu JY, Ruan QQ (2010) An illumination normalization model for face recognition under varied lighting conditions. Pattern Recogn Lett 31(9):1056-1067

    Article  Google Scholar 

  18. Chen CP, Chen CS.: (2005) Lighting normalization with generic intrinsic illumination subspace for face recognition. IEEE ICCV. 1-8

  19. Virendra P. Vishwakarma (2013) Illumination normalization using fuzzy filter in DCT domain for face recognition. Int J Mach Learn Cybern. doi:10.1007/s13042-013-0182-4

  20. Du S, Ward RK (2010) Adaptive region-based image enhancement method for robust face recognition under variable illumination conditions. IEEE trans Circuits Syst Video Technol 20(9):1165-1175

    Article  Google Scholar 

  21. Nabatchian A, Abdel-Raheem E, Ahmadi M (2011) Illumination invariant feature extraction and mutual-information-based local matching for face recognition under illumination variation and occlusion. Pattern Recogn, 44(10–11):2576-2587

    Article  Google Scholar 

  22. Basri R, Jacobs D (2003) Lambertian reflectance and linear subspaces. IEEE Trans Patt Anal Mach Intell 25(2):218-233

    Article  Google Scholar 

  23. Fiscler M, Bolles R (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM, 24:381-385

    Article  Google Scholar 

  24. Gnanadesikan R, Kettenring J.: (1972) Robust estimates, residuals, and outlier detection with multiresponse data Biometrics 28:81–124

    Google Scholar 

  25. Candès EJ, Li X, Ma Y, Wright J (2010) Robust principal component analysis? recovering low-rank matrices from sparse errors. IEEE Sensor Array and Multichannel Signal Processing Workshop 201–204

  26. Gonzales RC, Woods RE (2010) Digital image processing, 3nd ed. Prentice Hall

  27. http://cvc.yale.edu/projects/yalefaces/yalefaces.html

  28. http://cvc.yale.edu/projects/yalefacesB/yalefacesB.html

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Acknowledgments

This work is supported by the Natural Science Foundation of China under Grant: 61175041.

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Dalian University of Technology, Dalian, 116024, People’s Republic of China

    Yi Li & Xiaodong Liu

  2. Department of Computer and Information Engineering, Heilongjiang University of Science and Technology, Harbin, 150027, People’s Republic of China

    Yi Li & Zhijun Gao

Authors
  1. Yi Li
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  2. Xiaodong Liu
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  3. Zhijun Gao
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Correspondence to Yi Li.

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Li, Y., Liu, X. & Gao, Z. Shadow determination and compensation for face recognition. Int. J. Mach. Learn. & Cyber. 5, 599–605 (2014). https://doi.org/10.1007/s13042-013-0207-z

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  • DOI: https://doi.org/10.1007/s13042-013-0207-z

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