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Segmented Linear Subspaces for Illumination-Robust Face Recognition

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Abstract

All images of a convex Lambertian surface captured with a fixed pose under varying illumination are known to lie in a convex cone in the image space that is called the illumination cone. Since this cone model is too complex to be built in practice, researchers have attempted to approximate it with simpler models. In this paper, we propose a segmented linear subspace model to approximate the cone. Our idea of segmentation is based on the fact that the success of low dimensional linear subspace approximations of the illumination cone increases if the directions of the surface normals get close to each other. Hence, we propose to cluster the image pixels according to their surface normal directions and to approximate the cone with a linear subspace for each of these clusters separately. We perform statistical performance evaluation experiments to compare our system to other popular systems and demonstrate that the performance increase we obtain is statistically significant.

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References

  • Adini, Y., Moses, Y., and Ullman, S. 1997. Face recognition: The problem of compensating for changes in illumination direction. IEEE Trans. Pattern Analysis and Machine Intelligence, 19(7):721–732.

    Google Scholar 

  • Basri, R. and Jacobs, D. 2001. Lambertian reflectance and linear subspaces. In Proc. IEEE Conf. Computer Vision, pp. 383–390.

  • Batur, A.U. and Hayes, M.H. 2001. Linear subspaces for illumination robust face recognition. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 296–301.

  • Belhumeur, P.N., Hespanha, J.P., and Kriegman, D.J. 1997. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Trans. Pattern Analysis and Machine Intelligence, 19(7):711–720.

    Google Scholar 

  • Belhumeur, P.N. and Kriegman D.J. 1998. What is the set of images of an object under all possible illumination conditions? Int'l J. Computer Vision, 28(3):1–16.

    Google Scholar 

  • Belhumeur, P.N., Kriegman, D.J., and Yuille, A.L. 1999. The basrelief ambiguity. Int'l J. Computer Vision, 35(1):33–44.

    Google Scholar 

  • Beveridge, J.R., She, K., Draper, B.A., and Givens, G.H. 2001. A nonparametric statistical comparison of principal component and linear discriminant subspaces for face recognition. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 535–542.

  • Bichsel, M. 1995. Illumination invariant object recognition. In Proc. IEEE Int'l Conf. Image Processing, pp. 620–623.

  • Georghiades, A.S., Belhumeur, P.N., and Kriegman, D.J. 2001. From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Analysis and Machine Intelligence, 23(6):643–660.

    Google Scholar 

  • Hallinan, P. 1994. A low dimensional representation of human faces for arbitrary lighting conditions. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 995–999.

  • Lee, K., Ho, J., and Kriegman, D. 2001. Nine points of light: Acquiring subspaces for face recognition under variable lighting. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 519–526.

  • Micheals, R.J. and Boult, T.E. 2001. Efficient evaluation of classification and recognition systems. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 50–57.

  • Nayar, S.K. and Murase, H. 1996. Dimensionality of illumination in appearance matching. In Proc. IEEE Int'l Conf. Robotics and Automation, pp. 1326–1332.

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

    Google Scholar 

  • Ramamoorthi, R. 2002. Analytic PCA construction for theoretical analysis of lighting variability in images of a Lambertian object. IEEE Trans. Pattern Analysis and Machine Intelligence, 24(10):1322–1333.

    Google Scholar 

  • Shashua, A. 1997. On photometric issues in 3D visual recognition from a single 2D image. Int'l J. Computer Vision, 21(1–2):99–122.

    Google Scholar 

  • Sim, T., Baker, S., and Bsat, M. 2001. The CMU Pose, illumination, and expression (PIE) database of human faces. Tech Report CMU-RI-TR-01-02, The Robotics Institute, Carnegie Mellon University.

  • Zhao, L. and Yang, Y. 1999. Theoretical analysis of illumination in PCA-based vision systems. J. Pattern Recognition, 32(4):547–564.

    Google Scholar 

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Authors and Affiliations

  1. Center for Signal and Image Processing, School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, 30332

    A.U. Batur & M.H. Hayes III

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  1. A.U. Batur
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  2. M.H. Hayes III
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Batur, A., Hayes, M. Segmented Linear Subspaces for Illumination-Robust Face Recognition. International Journal of Computer Vision 57, 49–66 (2004). https://doi.org/10.1023/B:VISI.0000013090.39095.d5

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  • DOI: https://doi.org/10.1023/B:VISI.0000013090.39095.d5

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