In this paper, we propose a face pose normalization and simulation methods based on multi-view face alignment that can enhance the performance of the face recognition algorithm towards large pose variation. The proposed method includes two steps: 1) multi-view face alignment, 2) face pose normalization and simulation methods. Multi-view face alignment algorithm is inspired by the design idea of the Supervised Descent Method (SDM) which is considered the state-of-the-art in face alignment. The proposed method modified the algorithm to adapt multi-view problems by changing the histogram of gradient feature to projection of gradient feature in order to adapt large pose variance. In addition, the feature scale also can be adaptive adjusted towards different part of face, for example, eyes, mouth, eyebrows, etc. Based on the multi-view face alignment results, 2D face normalization and simulation methods are proposed. Experimental results over many images with obvious pose changes have shown our method can significantly normalize the multi-view pose face and improve the accuracy of the existing common face recognition method when faces of probe sets have large pose variation.