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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.

Keywords

Basis Image Gallery Image Spherical Harmonic Function Yale Face Database Adjusted Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

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).

References

  1. 1.
    Y. Gao, M.K. Leung, Face recognition using line edge map. IEEE Trans. Pattern Anal. Mach. Intell. 24(6), 764–779 (2002)CrossRefGoogle Scholar
  2. 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)CrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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. 498–505Google Scholar
  5. 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)MathSciNetCrossRefGoogle Scholar
  6. 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. 220–229Google Scholar
  7. 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)CrossRefGoogle Scholar
  8. 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)CrossRefGoogle Scholar
  9. 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)CrossRefGoogle Scholar
  10. 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)MathSciNetCrossRefGoogle Scholar
  11. 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)CrossRefGoogle Scholar
  12. 12.
    X. Xie, K.M. Lam, An efficient illumination normalization method for face recognition. Pattern Recogn. Lett. 27(6), 609–617 (2006)CrossRefGoogle Scholar
  13. 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. 995–999Google Scholar
  14. 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. 1326–1332Google Scholar
  15. 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)CrossRefGoogle Scholar
  16. 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)CrossRefGoogle Scholar
  17. 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)CrossRefGoogle Scholar
  18. 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)MathSciNetCrossRefGoogle Scholar
  19. 19.
    R. Basri, D.W. Jacobs, Lambertian reflectance and linear subspaces. IEEE Trans. Pattern Anal. Mach. Intell. 25(2), 218–233 (2003)CrossRefGoogle Scholar
  20. 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)CrossRefGoogle Scholar
  21. 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)CrossRefGoogle Scholar
  22. 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. 3546–3553Google Scholar
  23. 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. 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. 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–19Google Scholar
  26. 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–158Google Scholar
  27. 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. 209–216Google Scholar
  28. 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. 1177–1184Google Scholar
  29. 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. 1–8Google Scholar
  30. 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)CrossRefGoogle Scholar
  31. 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)CrossRefGoogle Scholar
  32. 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. 284–297Google Scholar
  33. 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. 118–129Google Scholar
  34. 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. 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)CrossRefGoogle Scholar
  36. 36.
    C.G. Atkeson, A.W. Moore, S. Schaal, Locally weighted learning for control, in Lazy Learning (Springer, New York, 1997), pp. 75–113CrossRefGoogle Scholar
  37. 37.
    L.L. Scharf, Statistical Signal Processing, vol. 98 (Addison-Wesley, Reading, 1991)zbMATHGoogle Scholar
  38. 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. 46–51Google Scholar
  39. 39.
    A.M. Martinez, The ar face database. CVC Technical Report, vol. 24 (1998)Google Scholar
  40. 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)CrossRefGoogle Scholar
  41. 41.
    E. Reinhard, K. Devlin, Dynamic range reduction inspired by photoreceptor physiology. IEEE Trans. Vis. Comput. Graph. 11(1), 13–24 (2005)CrossRefGoogle Scholar
  42. 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. 531–540Google Scholar
  43. 43.
    J.A. Nelder, R. Mead, A simplex method for function minimization. Comput. J. 7, 308–313 (1965)CrossRefzbMATHGoogle Scholar
  44. 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)MathSciNetCrossRefzbMATHGoogle Scholar
  45. 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. 340–351Google Scholar
  46. 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)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Electronics and InformationNorthwestern Polytechnical UniversityXi’anChina
  2. 2.Northwestern Polytechnical UniversityXi’anChina
  3. 3.Northwestern UniversityXi’anChina

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