Stereo reconstruction from image pairs is a genetic method for 3D acquisition of human faces. Depending on available imagery and reconstruction algorithm, the resulting 3D reconstructions may have deficits. We improve generic morphable model and fused with stereo reconstruction to remedy such deficits. Firstly, we obtain the face bounding box by Max-Margin Object Detection, and recognition the face feature points directly from a sparse subset of pixel intensities by the Ensemble of Regression Treesmethod. Secondly, through the color PCA model to generate the shape and color of the 3D facial statistical model, the ISOMAP algorithm is used to convert the 3D mesh to 2D surface and extract the texture information to get the facial model.Finally, the surface registration with two step non-rigid change on source mesh: we mean the global deformation in the source by using few anchor points on the source mesh, and using the Radial Basis Function (RBF) to express non-rigid global deformation; and then Procrustes analysis is applied for non-rigid transformation of the source vertex, the k-nearest neighbor transformation through weighted scheme tosmooth the local deformation. The experiment result shows the high-quality scan which is used for cloud comparison with the face model, stereo reconstruction and our deformed face model, after aligning all models with the high quality scan, three RMS facial deformation model values are 2.795 2, 2.102 8, 2.153 4.Comparing to other models, deformed face model has smaller discrepancies dominate more strongly, and the deformed face model is visually more shape consistent to the image. Qualitative and quantitative analysis of the deformed face model shows that the combination of stereo reconstruction with general shape information about human faces is geometrically superior to both the reconstruction based on single image from generic model as well as the stereo reconstruction without consideration of the generic model.