This paper proposes a new model of illumination variations of the object appearance, called the geodesic illumination basis model. It calculates pose-independent illumination bases on a 3D model, and these bases are warped into view-dependent bases in any pose. We experimentally evaluate how many illumination samples and bases are necessary, and show that our model can compensate for any illumination variations in any pose. A face recognition system incorporating our proposed model is constructed and its performance is tested using a database of 3D models and test images of 42 individuals captured in drastically differing pose and illumination conditions. Our system achieves a first-choice success ratio of 97.3% when the position and pose of the target face are known.