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Lighting Estimation and Adjustment for Facial Images

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Abstract

For robust face detection and recognition, the problem of lighting variation is considered as one of the greatest challenges. Lighting estimation and adjustment is a useful way to remove the influence of illumination for images. Due to the different prior knowledge provided by a single image and image sequences, algorithms dealing with lighting problems are always different for these two conditions. In this chapter we will present a lighting estimation algorithm for a single facial image and a lighting adjustment algorithm for image sequences. To estimate the lighting condition of a single facial image, a statistical model is proposed to reconstruct the lighting subspace where only one image of each subject is required. For lighting adjustment of image sequences, an entropy-based optimization algorithm is proposed to minimize the difference between consequent images. The effectiveness of those proposed algorithms are illustrated on face recognition, detection and tracking tasks.

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References

  1. Y. Gao, M.K. Leung, Face recognition using line edge map. IEEE Trans. Pattern Anal. Mach. Intell. 24(6), 764–779 (2002)

    Article  Google Scholar 

  2. H. Zhou, A.H. Sadka, Combining perceptual features with diffusion distance for face recognition. IEEE Trans. Syst. Man Cybern. Part C 41(5), 577–588 (2011)

    Article  Google Scholar 

  3. A. Shashua, T. Riklin-Raviv, The quotient image: class-based re-rendering and recognition with varying illuminations. IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 129–139 (2001)

    Article  Google Scholar 

  4. H. Wang, S.Z. Li, Y. Wang, Generalized quotient image, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2 (IEEE, New York, 2004), pp. 498–505

    Google Scholar 

  5. X. Xie, W.S. Zheng, J. Lai, P.C. Yuen, C.Y. Suen, Normalization of face illumination based on large-and small-scale features. IEEE Trans. Image Process. 20(7), 1807–1821 (2011)

    Article  MathSciNet  Google Scholar 

  6. X. Zhao, S.K. Shah, I.A. Kakadiaris, Illumination normalization using self-lighting ratios for 3d2d face recognition, in European Conference on Computer Vision (ECCV) Workshops and Demonstrations (Springer, New York, 2012), pp. 220–229

    Google Scholar 

  7. Y. Fu, N. Zheng, An appearance-based photorealistic model for multiple facial attributes rendering. IEEE Trans. Circuits Syst. Video Technol. 16(7), 830–842 (2006)

    Article  Google Scholar 

  8. M. De Marsico, M. Nappi, D. Riccio, H. Wechsler, Robust face recognition for uncontrolled pose and illumination changes. IEEE Trans. Syst. Man Cybern. Syst. 43(1), 149–163 (2013)

    Article  Google Scholar 

  9. T. Chen, W. Yin, X.S. Zhou, D. Comaniciu, T.S. Huang, Total variation models for variable lighting face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(9), 1519–1524 (2006)

    Article  Google Scholar 

  10. X. Tan, B. Triggs, Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19(6), 1635–1650 (2010)

    Article  MathSciNet  Google Scholar 

  11. W. Chen, M.J. Er, S. Wu, Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain. IEEE Trans. Syst. Man Cybern. B Cybern. 36(2), 458–466 (2006)

    Article  Google Scholar 

  12. X. Xie, K.M. Lam, An efficient illumination normalization method for face recognition. Pattern Recogn. Lett. 27(6), 609–617 (2006)

    Article  Google Scholar 

  13. P.W. Hallinan, A low-dimensional representation of human faces for arbitrary lighting conditions, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, New York, 1994), pp. 995–999

    Google Scholar 

  14. S.K. Nayar, H. Murase, Dimensionality of illumination in appearance matching, in IEEE International Conference on Robotics and Automation, vol. 2 (IEEE, New York, 1996), pp. 1326–1332

    Google Scholar 

  15. P.N. Belhumeur, D.J. Kriegman, What is the set of images of an object under all possible illumination conditions? Int. J. Comput. Vis. 28(3), 245–260 (1998)

    Article  Google Scholar 

  16. A.S. Georghiades, P.N. Belhumeur, D. Kriegman, From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 643–660 (2001)

    Article  Google Scholar 

  17. S.K. Zhou, G. Aggarwal, R. Chellappa, D.W. Jacobs, Appearance characterization of linear lambertian objects, generalized photometric stereo, and illumination-invariant face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 29(2), 230–245 (2007)

    Article  Google Scholar 

  18. R. Ramamoorthi, P. Hanrahan, On the relationship between radiance and irradiance: determining the illumination from images of a convex lambertian object. J. Opt. Soc. Am. A 18(10), 2448–2459 (2001)

    Article  MathSciNet  Google Scholar 

  19. R. Basri, D.W. Jacobs, Lambertian reflectance and linear subspaces. IEEE Trans. Pattern Anal. Mach. Intell. 25(2), 218–233 (2003)

    Article  Google Scholar 

  20. C.P. Chen, C.S. Chen, Intrinsic illumination subspace for lighting insensitive face recognition. IEEE Trans. Syst. Man Cybern. B Cybern. 42(2), 422–433 (2012)

    Article  Google Scholar 

  21. A. Wagner, J. Wright, A. Ganesh, Z. Zhou, H. Mobahi, Y. Ma, Toward a practical face recognition system: robust alignment and illumination by sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 34(2), 372–386 (2012)

    Article  Google Scholar 

  22. L. Zhuang, A.Y. Yang, Z. Zhou, S.S. Sastry, Y. Ma, Single-sample face recognition with image corruption and misalignment via sparse illumination transfer, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, New York, 2013), pp. 3546–3553

    Google Scholar 

  23. L. Li, S. Li, Y. Fu, Discriminative dictionary learning with low-rank regularization for face recognition, in 10th IEEE International Conference Automatic Face and Gesture Recognition (2013)

    Google Scholar 

  24. Y. Zhang, M. Shao, E. Wong, Y. Fu, Random faces guided sparse many-to-one encoder for pose-invariant face recognition, in International Conference on Computer Vision (ICCV) (2013)

    Google Scholar 

  25. L. Zhang, D. Samaras, Face recognition under variable lighting using harmonic image exemplars, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1 (IEEE, New York, 2003), I–19

    Google Scholar 

  26. Z. Wen, Z. Liu, T.S. Huang, Face relighting with radiance environment maps, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2 (IEEE, New York, 2003), II–158

    Google Scholar 

  27. L. Zhang, S. Wang, D. Samaras, Face synthesis and recognition from a single image under arbitrary unknown lighting using a spherical harmonic basis morphable model, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2 (IEEE, New York, 2005), pp. 209–216

    Google Scholar 

  28. J. Lee, J. Moghaddam, H. Pfister, R. Machiraju, A bilinear illumination model for robust face recognition, in International Conference on Computer Vision (ICCV), vol. 2 (IEEE, New York, 2005), pp. 1177–1184

    Google Scholar 

  29. Y. Wang, Z. Liu, G. Hua, Z. Wen, Z. Zhang, D. Samaras, Face re-lighting from a single image under harsh lighting conditions, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, New York, 2007), pp. 1–8

    Google Scholar 

  30. X. Zhao, G. Evangelopoulos, D. Chu, S. Shah, I.A. Kakadiaris, Minimizing illumination differences for 3d to 2d face recognition using lighting maps. IEEE Trans. Cybern. 44(5), 725–736 (2014)

    Article  Google Scholar 

  31. K.C. Lee, J. Ho, D. Kriegman, Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans. Pattern Anal. Mach. Intell. 27(5), 684–698 (2005)

    Article  Google Scholar 

  32. X. Jiang, Y.O. Kong, J. Huang, R. Zhao, Y. Zhang, Learning from real images to model lighting variations for face images, in European Conference on Computer Vision (ECCV) (2008), pp. 284–297

    Google Scholar 

  33. X. Jiang, P. Fan, I. Ravyse, H. Sahli, J. Huang, R. Zhao, Y. Zhang, Perception-based lighting adjustment of image sequences, in Asian Conference on Computer Vision (ACCV) (2009), pp. 118–129

    Google Scholar 

  34. T. Sim, T. Kanade, Combining models and exemplars for face recognition: an illuminating example, in Proceedings of the CVPR 2001 Workshop on Models versus Exemplars in Computer Vision, vol. 1 (2001)

    Google Scholar 

  35. Y. Adini, Y. Moses, S. Ullman, Face recognition: the problem of compensating for changes in illumination direction. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 721–732 (1997)

    Article  Google Scholar 

  36. C.G. Atkeson, A.W. Moore, S. Schaal, Locally weighted learning for control, in Lazy Learning (Springer, New York, 1997), pp. 75–113

    Book  Google Scholar 

  37. L.L. Scharf, Statistical Signal Processing, vol. 98 (Addison-Wesley, Reading, 1991)

    MATH  Google Scholar 

  38. T. Sim, S. Baker, M. Bsat, The cmu pose, illumination, and expression (pie) database, in Fifth IEEE International Conference on Automatic Face and Gesture Recognition (IEEE, New York, 2002), pp. 46–51

    Google Scholar 

  39. A.M. Martinez, The ar face database. CVC Technical Report, vol. 24 (1998)

    Google Scholar 

  40. H. Han, S. Shan, X. Chen, W. Gao, A comparative study on illumination preprocessing in face recognition. Pattern Recogn. 46(6), 1691–1699 (2013)

    Article  Google Scholar 

  41. E. Reinhard, K. Devlin, Dynamic range reduction inspired by photoreceptor physiology. IEEE Trans. Vis. Comput. Graph. 11(1), 13–24 (2005)

    Article  Google Scholar 

  42. X. Jiang, P. Sun, R. Xiao, R. Zhao, Perception based lighting balance for face detection, in Asia Conference on Computer Vision, ACCV06 (2006), pp. 531–540

    Google Scholar 

  43. J.A. Nelder, R. Mead, A simplex method for function minimization. Comput. J. 7, 308–313 (1965)

    Article  MATH  Google Scholar 

  44. R.A. Waltz, J.L. Morales, J. Nocedal, D. Orban, An interior algorithm for nonlinear optimization that combines line search and trust region steps. Math. Program. 107(3), 391–408 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  45. Y. Hou, H. Sahli, I. Ravyse, Y. Zhang, R. Zhao, Robust shape-based head tracking, in Proceedings of the Advanced Concepts for Intelligent Vision Systems (ACIVS 2007), eds. by J. Blanc-Talon, W. Philips, D. Popescu, P. Scheunders. Springer Lecture Notes in Computer Science, vol. 4678 (2007), pp. 340–351

    Google Scholar 

  46. F. Dornaika, F. Davoine, Simultaneous facial action tracking and expression recognition in the presence of head motion. Int. J. Comput. Vis. 76(3), 257–281 (2008)

    Article  Google Scholar 

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Acknowledgements

This material is based upon work supported by the PhD Programs Foundation of Ministry of Education of China (Grant No. 20136102120041, 20116102120031), National High-tech Research and Development Program of China(863 Program) (No. 2014AA015201), National Natural Science Foundation of China (No. 61103062, No. 61502388), and the Fundamental Research Funds for the Central Universities (No. 3102015BJ(II)ZS016).

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Authors and Affiliations

  1. School of Electronics and Information, Northwestern Polytechnical University, Xi’an, 710072, China

    Xiaoyue Jiang

  2. Northwestern Polytechnical University, Xi’an, 710072, China

    Xiaoyi Feng & Jun Wu

  3. Northwestern University, Xi’an, 710069, China

    Jinye Peng

Authors
  1. Xiaoyue Jiang
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  2. Xiaoyi Feng
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  3. Jun Wu
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  4. Jinye Peng
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Correspondence to Xiaoyue Jiang .

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Editors and Affiliations

  1. Automatic Control, Silesian University of Technology, Kawulok, Poland

    Michal Kawulok

  2. Computer Science, Louisiana State University in Shreveport, Shreveport, Louisiana, USA

    M. Emre Celebi

  3. Silesian University of Technology, Gliwice, Poland

    Bogdan Smolka

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Jiang, X., Feng, X., Wu, J., Peng, J. (2016). Lighting Estimation and Adjustment for Facial Images. In: Kawulok, M., Celebi, M., Smolka, B. (eds) Advances in Face Detection and Facial Image Analysis. Springer, Cham. https://doi.org/10.1007/978-3-319-25958-1_3

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  • DOI: https://doi.org/10.1007/978-3-319-25958-1_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25956-7

  • Online ISBN: 978-3-319-25958-1

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