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Separability Oriented Preprocessing for Illumination-Insensitive Face Recognition

  • Hu Han
  • Shiguang Shan
  • Xilin Chen
  • Shihong Lao
  • Wen Gao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7578)

Abstract

In the last decade, some illumination preprocessing approaches were proposed to eliminate the lighting variation in face images for lighting-invariant face recognition. However, we find surprisingly that existing preprocessing methods were seldom modeled to directly enhance the separability of different faces, which should have been the essential goal. To address the issue, we propose to explicitly exploit maximizing separability of different subjects’ faces as the preprocessing objective. With this in mind, a novel approach, named by us Separability Oriented Preprocessing (SOP), is proposed to enhance face images by maximizing the Fisher separability criterion in scale-space. Extensive experiments on both laboratory-controlled and real-world face databases using different recognition methods show the effectiveness of the proposed approach.

Keywords

Separability oriented illumination preprocessing lighting-invariant face recognition 

References

  1. 1.
    Li, S.Z., Jain, A.K. (eds.): Handbook of Face Recognition, 2nd edn. Springer, London (2011)zbMATHGoogle Scholar
  2. 2.
    Chen, H.F., Belhumeur, P.N., Jacobs, D.W.: In Search of Illumination Invariants. In: IEEE CVPR, pp. 1254–1261. IEEE Press, South Carolina (2000)Google Scholar
  3. 3.
    Shashua, A., Riklin Raviv, T.: The Quotient Image: Class-Based Re-Rendering and Recognition with Varying Illuminations. IEEE Trans. PAMI 23, 129–139 (2001)CrossRefGoogle Scholar
  4. 4.
    Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution Gray-scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Trans. PAMI 24, 971–987 (2002)CrossRefGoogle Scholar
  5. 5.
    Wang, H., Li, S., Wang, Y.: Face Recognition under Varying Lighting Conditions Using Self Quotient Image. In: IEEE FG, pp. 819–824. IEEE Press, Seoul (2004)Google Scholar
  6. 6.
    Tan, X., Triggs, B.: Enhanced Local Texture Feature Sets for Face Recognition under Difficult Lighting Conditions. IEEE Trans. Image Process 19, 1635–1650 (2010)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Biswas, S., Aggarwal, G., Chellappa, R.: Robust Estimation of Albedo for Illumination-Invariant Matching and Shape Recovery. IEEE Trans. PAMI 31, 884–899 (2009)CrossRefGoogle Scholar
  8. 8.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans. PAMI 19, 711–720 (1997)CrossRefGoogle Scholar
  9. 9.
    Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose. IEEE Trans. PAMI 23, 643–660 (2001)CrossRefGoogle Scholar
  10. 10.
    Basri, R., Jacobs, D.W., Kriegman, D.J.: Lambertian Reflectance and Linear Subspaces. IEEE Trans. PAMI 25, 218–233 (2003)CrossRefGoogle Scholar
  11. 11.
    Zhang, L., Samaras, D.: Face Recognition from a Single Training Image under Arbitrary Unknown Lighting Using Spherical Harmonics. IEEE Trans. PAMI 28, 351–363 (2006)CrossRefGoogle Scholar
  12. 12.
    Zhou, S.K., Aggarwal, G., Chellappa, R., Jacobs, D.W.: Appearance Characterization of Linear Lambertian Objects, Generalized Photometric Stereo, and Illumination-Invariant Face Recognition. IEEE Trans. PAMI 29, 230–245 (2007)CrossRefGoogle Scholar
  13. 13.
    Wang, Y., Liu, Z., Hua, G., Wen, Z., Zhang, Z., Samaras, D.: Face Re-Lighting from a Single Image under Harsh Lighting Conditions. In: IEEE CVPR, pp. 1–8. IEEE Press, Minnesota (2007)Google Scholar
  14. 14.
    Kim, J.Y., Kim, L.S., Hwang, S.H.: An Advanced Contrast Enhancement Using Partially Overlapped Sub-Block Histogram Equalization. IEEE Trans. CSVT 11, 475–484 (2001)Google Scholar
  15. 15.
    Solar, J., Navarrete, P.: Eigenspace-Based Face Recognition: A Comparative Study of Different Approaches. IEEE Trans. SMC:C 35, 315–325 (2005)Google Scholar
  16. 16.
    Chen, W., Er, M.J., Wu, S.: Illumination Compensation and Normalization for Robust Face Recognition Using Discrete Cosine Transform in Logarithm Domain. IEEE Trans. SMC:B 36, 458–466 (2006)Google Scholar
  17. 17.
    Xie, X., Zheng, W., Lai, J., Yuen, P.C.: Face Illumination Normalization on Large and Small Scale Features. In: IEEE CVPR, pp. 1–8. IEEE Press, Alaska (2008)Google Scholar
  18. 18.
    Han, H., Shan, S., Qing, L., Chen, X., Gao, W.: Lighting Aware Preprocessing for Face Recognition across Varying Illumination. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part II. LNCS, vol. 6312, pp. 308–321. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  19. 19.
    Ramamoorthi, R.: Analytic PCA Construction for Theoretical Analysis of Lighting Variability in Images of a Lambertian Object. IEEE Trans. PAMI 24, 1–12 (2002)CrossRefGoogle Scholar
  20. 20.
    Chan, T.F., Esedoglu, S.: Aspects of Total Variation Regularized L1 Function Approximation. SIAM J. Appl. Math. 65, 1817–1837 (2005)MathSciNetzbMATHCrossRefGoogle Scholar
  21. 21.
    Chen, T., Yin, W., Zhou, X.S., Comaniciu, D., Huang, T.S.: Total Variation Models for Variable Lighting Face Recognition. IEEE Trans. PAMI 28, 1519–1524 (2006)CrossRefGoogle Scholar
  22. 22.
    Beveridge, J.R., Draper, B.A., Chang, J., Kirby, M., Kley, H., Peterson, C.: Principal Angles Separate Subject Illumination Spaces in YDB and CMU-PIE. IEEE Trans. PAMI 31, 351–356 (2009)CrossRefGoogle Scholar
  23. 23.
    Gross, R., Matthews, I., Cohn, J., Kanade, T., Baker, S.: Multi-PIE. Image and Vision Computing 28, 807–813 (2010)CrossRefGoogle Scholar
  24. 24.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley Interscience, New York (2001)zbMATHGoogle Scholar
  25. 25.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220, 671–680 (1983)MathSciNetzbMATHCrossRefGoogle Scholar
  26. 26.
    Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the Face Recognition Grand Challenge. In: IEEE CVPR, pp. 947–954. IEEE Press, San Diego (2005)Google Scholar
  27. 27.
    Turk, M., Pentland, A.: Eigenfaces for Recognition. J. Cognit. Neurosci. 3, 71–86 (1991)CrossRefGoogle Scholar
  28. 28.
    Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust Face Recognition via Sparse Representation. IEEE Trans. PAMI 31, 210–227 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hu Han
    • 1
    • 2
  • Shiguang Shan
    • 1
  • Xilin Chen
    • 1
  • Shihong Lao
    • 3
  • Wen Gao
    • 4
    • 1
  1. 1.Institute of Computing Technology, CASKey Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS)BeijingChina
  2. 2.Department of Computer Science and EngineeringMichigan State UniversityEast LansingU.S.A.
  3. 3.Omron Social Solutions Co., LTD.KyotoJapan
  4. 4.Institute of Digital MediaPeking UniversityBeijingChina

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