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
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)
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)
Basri, R., Jacobs, D.: Lambertian reflectance and linear subspaces. IEEE Trans. Pattern Anal. Mach. Intell. 25(2), 218–233 (2003)
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)
Blanz, V., Vetter, T.: A morphable model for the synthesis of 3D faces. In: Proc. ACM SIGGRAPH (1999)
Blanz, V., Scherbaum, K., Vetter, T., Seidel, H.: Exchanging faces in images. Presented at the Proc. EuroGraphics (2004)
Vasilescu, M.A.O., Terzopoulos, D.: Multilinear analysis of image ensembles: TensorFaces. In: Proc. IEEE ECCV (2002)
Lee, J., Moghaddam, B., Pfister, H., Machiraju, R.: A bilinear illumination model for robust face recognition. In: Proc. IEEE Int. Conf. Computer Vision (2005)
Barrow, H.G., Tenenbaum, J.M.: Recovering Intrinsic scene Characteristics from Images. Computer Vision System (1978)
Weiss, Y.: Deriving Intrinsic Images from Image Sequences. In: Proc. of IEEE ICCV (2001)
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)
Tuchin, V.: Tissue Optics: Light Scattering Methods and Instruments for Medical Diagnosis. SPIE Press, Bellingham (2000)
Anderson, R., Parrish, J.: The Optics of Human Skin. J. Investigative Dermatology 77(1), 13–19 (1981)
Gemert, M., Jacques, S., Sternborg, H., Star, W.: Skin Optics. IEEE Trans. Biomedical Eng. 36(12), 1146–1154 (1989)
Angelopoulou, E., Molana, R., Daniilidis, K.: Multispectral Skin Color for Modeling. In: Proc. of IEEE International Conference on Computer Vision and Pattern Recognition (2001)
Pan, Z., Healey, G., Prasad, M., Tromberg, B.: Face Recognition in Hyperspectral Images. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 1552–1560 (2003)
Jia, K., Gong, S.: Multi-modal tensor face for simultaneous super-resolution and recognition. In: Proc. IEEE Int. Conf. Computer Vision (2005)
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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
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