Subspace technique is an efficient method for automatic facial attractiveness analysis. To enhance the intrinsic description for facial attractiveness, a dual subspace method on the subspaces of PCA and Generalized Low Rank Approximation Matrix (GLRAM) is proposed. Thus, their individual characteristics in characterizing the global and local intrinsic description of facial attractiveness can be collaboratively boosted. In addition, the Gaussian Field model (GF) is applied to reflect the geometry structure in sample space. The experiment is carried on a challenging database, which takes on significant variations in the aspects of illumination, background, facial expression, age, race, and so on. The experimental results show the advantages of the proposed dual subspace method for facial attractiveness analysis over individual subspace.