Illumination variation remains a central challenge in object detection and
recognition. Existing analyses of illumination variation typically pertain to
convex, Lambertian objects, and guarantee quality of approximation in an
average case sense. We show that it is possible to build V(vertex)-description
convex cone models with worst-case performance guarantees, for non-convex
Lambertian objects. Namely, a natural verification test based on the angle to
the constructed cone guarantees to accept any image which is sufficiently
well-approximated by an image of the object under some admissible lighting
condition, and guarantees to reject any image that does not have a sufficiently
good approximation. The cone models are generated by sampling point
illuminations with sufficient density, which follows from a new perturbation
bound for point images in the Lambertian model. As the number of point images
required for guaranteed verification may be large, we introduce a new
formulation for cone preserving dimensionality reduction, which leverages tools
from sparse and low-rank decomposition to reduce the complexity, while
controlling the approximation error with respect to the original cone