3. The face recognition method according to claim 1 , wherein steps for reconstructing said 3D face model for said original face image include: constructing said 3D morphable face model, wherein said 3D morphable face model comprises a 3D geometry data set and a 3D texture data set; expressing said 3D morphable face model by a linear combination of a plurality of eigen head basis vectors; and obtaining a geometry data set, a texture data set and 3D characteristic parameters by photometric error minimization for reconstructing said 3D face model corresponding to said original face image.
3. The face recognition method according to claim 1 , wherein steps for reconstructing said 3D face model for said original face image include: constructing said 3D morphable face model, wherein said 3D morphable face model comprises a 3D geometry data set and a 3D texture data set; expressing said 3D morphable face model by a linear combination of a plurality of eigen head basis vectors; and obtaining a geometry data set, a texture data set and 3D characteristic parameters by photometric error minimization for reconstructing said 3D face model corresponding to said original face image. 6. The face recognition method according to claim 3 , wherein said geometry data set is: S ⁡ ( α ) = S _ + ∑ m i = 1 ⁢ α i ⁢ s i ; ⁢ and said texture data set is: T ⁡ ( β ) = T _ + ∑ m i = 1 ⁢ β i ⁢ t i , wherein S is the mean shape vector, T is the mean texture vector, s i is the i th eigen-shape basis vector, t i is the i th eigen-texture basis vector, α=[α 1 , . . . , α m ] are the corresponding coefficients of said eigen-shape basis vectors and β=[β 1 , . . . , β m ] are the corresponding coefficients of said eigen-texture basis vectors.