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Face Relighting Based on Multi-spectral Quotient Image and Illumination Tensorfaces

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 5996)

Abstract

In this paper, a new approach to face relighting by the product of reflectance image and illumination Tensorfaces is proposed. With a pair of multi-spectral images, a near infrared and a visual image, the intrinsic images decomposition can be implemented and corresponding reflectance image is derived. Besides, the illumination images obtained from last step as well as the input visual images constitute a 3-D tensor, on which super-resolution and maximum a posteriori probability estimation are carried out. And then, illumination Tensorfaces under specific light are derived, by which face under target illumination can be synthesized. In contrast to commonly used shape models or shape dependent models, the proposed method only relies on Lambertian assumption and manages to recover reflectance of the face. Besides, compared with the existing methods, i.e. Tensorfaces and Quotient Image, our methods properly preserve the identity of the subject as well as the texture details. Experiments show that the proposed method is not only simple when deriving intrinsic images, but also practical when performing face relighting.

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References

  1. 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. Pattern Anal. Mach. Intell. 23(6), 643–660 (2001)

    CrossRef  Google Scholar 

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

    CrossRef  Google Scholar 

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

    CrossRef  Google Scholar 

  4. Ramamoorthi, R., Hanrahan, P.: On the relationship between radiance and irradiance: determining the illumination from images of a convex Lambertian object. JOSA A 18(10), 2448–2459 (2001)

    CrossRef  MathSciNet  Google Scholar 

  5. Blanz, V., Vetter, T.: A morphable model for the synthesis of 3D faces. In: Proc. ACM SIGGRAPH (1999)

    Google Scholar 

  6. Blanz, V., Scherbaum, K., Vetter, T., Seidel, H.: Exchanging faces in images. Presented at the Proc. EuroGraphics (2004)

    Google Scholar 

  7. Vasilescu, M.A.O., Terzopoulos, D.: Multilinear analysis of image ensembles: TensorFaces. In: Proc. IEEE ECCV (2002)

    Google Scholar 

  8. Lee, J., Moghaddam, B., Pfister, H., Machiraju, R.: A bilinear illumination model for robust face recognition. In: Proc. IEEE Int. Conf. Computer Vision (2005)

    Google Scholar 

  9. Barrow, H.G., Tenenbaum, J.M.: Recovering Intrinsic scene Characteristics from Images. Computer Vision System (1978)

    Google Scholar 

  10. Weiss, Y.: Deriving Intrinsic Images from Image Sequences. In: Proc. of IEEE ICCV (2001)

    Google Scholar 

  11. Shim, H., Luo, J., Chen, T.: A Subspace Model-Based Approach to Face Relighting Under Unknown Lighting and Poses. IEEE Trans. Image Process. 17(8), 1331–1341 (2008)

    CrossRef  MathSciNet  Google Scholar 

  12. Tuchin, V.: Tissue Optics: Light Scattering Methods and Instruments for Medical Diagnosis. SPIE Press, Bellingham (2000)

    Google Scholar 

  13. Anderson, R., Parrish, J.: The Optics of Human Skin. J. Investigative Dermatology 77(1), 13–19 (1981)

    CrossRef  Google Scholar 

  14. Gemert, M., Jacques, S., Sternborg, H., Star, W.: Skin Optics. IEEE Trans. Biomedical Eng. 36(12), 1146–1154 (1989)

    CrossRef  Google Scholar 

  15. Angelopoulou, E., Molana, R., Daniilidis, K.: Multispectral Skin Color for Modeling. In: Proc. of IEEE International Conference on Computer Vision and Pattern Recognition (2001)

    Google Scholar 

  16. Pan, Z., Healey, G., Prasad, M., Tromberg, B.: Face Recognition in Hyperspectral Images. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 1552–1560 (2003)

    CrossRef  Google Scholar 

  17. Jia, K., Gong, S.: Multi-modal tensor face for simultaneous super-resolution and recognition. In: Proc. IEEE Int. Conf. Computer Vision (2005)

    Google Scholar 

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Shao, M., Wang, Y., Liu, P. (2010). Face Relighting Based on Multi-spectral Quotient Image and Illumination Tensorfaces. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12297-2_11

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  • DOI: https://doi.org/10.1007/978-3-642-12297-2_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12296-5

  • Online ISBN: 978-3-642-12297-2

  • eBook Packages: Computer ScienceComputer Science (R0)