Improving 3D face reconstruction from a single image using half-frontal face poses

In this work we evaluate the influence of pose variation on 3D face reconstruction from a single image. To this end, we present a 3D reconstruction method that combines a fitting technique and a sparse 3D deformable model to estimate the 3D information of 2D images with large pose variations. For our experiments, we synthetically created 2D images by rendering 3D models from the BU-3DFE database in different points of view. Thus, we have a precise ground truth that allows performing a quantitative analysis of the reconstruction accuracy. Our experimental results show that the reconstruction achieves the highest accuracy when using half-frontal face images, and is also more robust to noise and incorrect facial landmarks positioning.