This paper focuses on the detection of objects with a Lambertian surface under varying illumination and pose. We offer to apply a novel detection method that proceeds by modeling the different illuminations from a small number of images in a training set; this automatically voids the illumination effects, allowing fast illumination invariant detection, without having to create a large training set. It is demonstrated that the method ``fits in'' nicely with previous work about modeling the set of object appearances under varying illumination. In the experiments, an object was correctly detected under image plane rotations in a 45