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To deal with image variations due to illumination problem, Ramamoorthi and Basri have independently derived a spherical harmonic analysis for the Lambertian reflectance and linear subspace. Their theoretical work provided a new approach for face representation, however both of them assume that the 3D surface normals and albedo (or unit albedo) are known, which limit this algorithm's application. We present a novel method for modelling 3D face shape and albedo from only three images and this work will fill the blank which Ramamoorthi and Basri left. Our work is closely related to photometric stereo, but conditions of photometric stereo for estimating albedo and surface normal are too strict to be applied for real application. Moreover, the conventionally used singular value decomposition (SVD) approach leads to the notorious linear ambiguity and the solution to this problem needs to introduce more constraints or more images. By taking advantage of similar 3D shape of all human faces, the highlight of the new method is that it circumambulates the linear ambiguity by 3D alignment. The experiment results show that our estimated model can be perfectly employed to face recognition and 3D reconstruction.