This paper describes a new face recognition method using a projection-based 3D normalization and a shading subspace orthogonalization under variation in facial pose and illumination. The proposed method does not need any reconstruction and reillumination for a personalized 3D model, thus it can avoid these troublesome problems and the recognition process can be done rapidly. The facial size and pose including out of plane rotation can be normalized to a generic 3D model from one still image and the input subspace is generated by perturbed cropped patterns in order to absorb the localization errors. Furthermore, by exploiting the fact that a normalized pattern is fitted to the generic 3D model, illumination robust features are extracted through the shading subspace orthogonalization. Evaluation experiments are performed using several databases and the results show the effectiveness of our method under various facial poses and illuminations.