In this paper, we propose a novel algorithm for reconstructing the 3D shape and texture of human faces from two stereo images, which are captured from calibrated cameras. Our approach works in a sparse to dense manner: we first build a coarse shape estimation based on 3D keypoints, and then use a linear morphable model to efficiently match the detail shape and texture. Compared with the previous works, our algorithm can reconstruct the 3D face shape in a speed comparable with that of the fastest algorithm available, but gives a higher accuracy. It can also recover the texture with more complete, realistic looking. Our results show that the new algorithm possesses significant characteristics of a 3D face model reconstruction system, and is especially useful for face recognition and animation applications in practice.