Most existing pose-independent Face Recognition (FR) techniques take advantage of 3D model to guarantee the naturalness while normalizing or simulating pose variations. Two nontrivial problems to be tackled are accurate measurement of pose parameters and computational efficiency. In this paper, we introduce an effective and efficient approach to estimate human head pose, which fundamentally ameliorates the performance of 3D aided FR systems. The proposed method works in a progressive way: firstly, a random forest (RF) is constructed utilizing synthesized images derived from 3D models; secondly, the classification result obtained by applying well-trained RF on a probe image is considered as the preliminary pose estimation; finally, this initial pose is transferred to shape-based 3D morphable model (3DMM) aiming at definitive pose normalization. Using such a method, similarity scores between frontal view gallery set and pose-normalized probe set can be computed to predict the identity. Experimental results achieved on the UHDB dataset outperform the ones so far reported. Additionally, it is much less time-consuming than prevailing 3DMM based approaches.