Unconstrained face recognition under varying views is one of the most challenging tasks, since the difference in appearances caused by poses may be even larger than that due to identity. In this paper, we exploit and analyze a novel pose normalization scheme for facial images under varying views via robust 3D shape reconstruction from single, unconstrained photos in the wild. Specifically, to address the problem of ambiguous 2D-to-3D landmark correspondence and imperfect landmark detector, for each input 2D face, the 3D shape is suggested to be learned by iteratively refining the 3D landmarks and the weighting coefficients of each landmark. Experimental results on both LFW and a large-scale self-collected face databases demonstrate that the proposed approach performs better than the existing representative technologies.