Illumination Cones for Recognition under Variable Lighting: Faces

Due to illumination variability, the same object can appear dramatically di erent even when viewed in xed pose. To handle this variability, an object recognition system must employ a representation that is either invariant to, or models this variability. This paper presents an appearance-based method for modeling the variability due to illumination in the images of objects. The method di ers from past appearance-based methods, however, in that a small set of training images is used to generate a representation { the illumination cone { which models the complete set of images of an object with Lambertian re ectance under an arbitrary combination of point light sources at in nity. This method is both an implementation and extension (an extension in that it models cast shadows) of the illumination cone representation proposed in [3]. The method is tested on a database of 660 images of 10 faces, and the results exceed those of popular existing methods.