The 3D facial expression analysis and synthesis become one of the traditional technics used in face recognition systems which lead to get results with high performance under the varying poses or expressions of the face. In this paper, we propose a new system to generate facial expressions and poses from a single captured image. Expressions are based on the six prototypes,, as well as the synthesis of different types of facial poses.Then, using them in face recognition task where only a single image is available is the main methodology. The 3D model was implemented using the process of connecting set of points as triangle meshes in a 3D space. Data sets include the indices and vertices of the triangles. 20 geometrical feature points on the face were used depict the feature of the neutral expression of the face image. In this paper, the analysis are based on facial expression and poses synthesis both using Local Binary Pattern Histograms (LBPH) and Support Vector Machine (SVM). The synthesis poses and expressions of the model, which are artificial faces have been evaluated by using two different databases including the BU-3DFE and FEI database.The artificial faces are used for face recognition task, where only single face image is enough for the subjects. The average recognition rates on the BU-3DFE yielded more performance compared to the FEI database, where the average is 78.57% which is significantly 5% more than the FEI database average The recognition rate on the FEI database was reported as 71.78%. Overall, the proposed method attempts to solve the face recognition problem under the limitation of having only single image available. Artificially synthesized images are then used to build the recognition system. Results show that the system achieves satisfactory performance by using only one face image for each subject.