This paper proposes a novel multi-view 3D point cloud face model reconstruction system which can well capture local asymmetry features to deal with the occlusion problem often happening in single-view-based approaches. With the help of asymmetries between facial landmarks to register 3D point clouds, this system can significantly improve the reconstruction rate even though the offsets or transformations between head poses are large. Moreover, its accuracy can be further improved via a face segmentation method to exclude non-facial elements and filter out facial outlier. Experimental results show the average distance between could points is 0.17mm which outperforms than other SoTA techniques, and 98.2 percent accuracy on face recognition is achieved.