Face recognition in the presence of multiple illumination sources

Most existing face recognition algorithms work well for controlled images but are quite susceptible to changes in illumination and pose. This has led to the rise of analysis-by-synthesis approaches due to their inherent potential to handle these external factors. Though these approaches work quite well, most of them assume that the face is illuminated by a single light source which is usually not true in realistic conditions. In this paper, we propose an algorithm to recognize faces illuminated by arbitrarily placed, multiple light sources. The algorithm does not need to know the number of light sources and works extremely well even while recognizing faces illuminated by different number of light sources. Results using this algorithm are reported on multiple-illumination datasets generated from PIE by T. Sim, et al. (2003) and Yale Face Database B by W. Zhao, et al. (2003). We also highlight the importance of the hard non-linearity in the Lambert's law which is often ignored, probably to linearize the estimation process

[1]  David J. Kriegman,et al.  From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  David J. Kriegman,et al.  Nine points of light: acquiring subspaces for face recognition under variable lighting , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[3]  Amnon Shashua,et al.  The Quotient Image: Class-Based Re-Rendering and Recognition with Varying Illuminations , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  P. Hanrahan,et al.  On the relationship between radiance and irradiance: determining the illumination from images of a convex Lambertian object. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[5]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression Database , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Thomas Vetter,et al.  Face Recognition Based on Fitting a 3D Morphable Model , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[8]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[9]  Lei Zhang,et al.  Face recognition under variable lighting using harmonic image exemplars , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[10]  Rama Chellappa,et al.  Characterization of Human Faces under Illumination Variations Using Rank, Integrability, and Symmetry Constraints , 2004, ECCV.

[11]  Rama Chellappa,et al.  Symmetric Shape-from-Shading Using Self-ratio Image , 2001, International Journal of Computer Vision.

[12]  Amnon Shashua,et al.  On Photometric Issues in 3D Visual Recognition from a Single 2D Image , 2004, International Journal of Computer Vision.

[13]  Tomaso A. Poggio,et al.  Example-Based Learning for View-Based Human Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Ronen Basri,et al.  Lambertian Reflectance and Linear Subspaces , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Ronen Basri,et al.  Photometric stereo with general, unknown lighting , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[16]  Daniel Snow,et al.  Determining Generative Models of Objects Under Varying Illumination: Shape and Albedo from Multiple Images Using SVD and Integrability , 1999, International Journal of Computer Vision.

[17]  Wu Ling,et al.  Example-based Learning for Human Face Detection , 2002 .