Evaluations of both academic face recognition algorithms and commercial systems have shown that the recognition performance degrades significantly due to the variation of illumination. Previous methods for illumination robust face recognition usually involve computationally expensive 3D model transformations or optimization base reconstruction using multiple gallery face images, making them infeasible in practical large scale face identification applications. In this paper, we propose an alternative face identification framework, in which one image per person is used for enrollment as is commonly practiced in real life applications. Several probe images captured under different illumination conditions are synthesized to imitate the illumination condition of the enrolled gallery face image. We assume Lambertian reflectance of human faces and use the harmonic representations of lighting. We demonstrate satisfactory performance on the Yale B database, both visually and quantitatively. The proposed method is of very low complexity when linear facial feature are used, and is therefore scalable for large scale applications.