A METHOD FOR SYNTHESIS OF A 3D FACE USING A SINGLE 2D IMAGE
3D facial reconstruction systems create 3D facial computer models of individuals from their 2D photographic images or video sequences. Currently, published face recognition systems are mostly based on large training sets of 2D facial images although there has been an increase in interest in using 3D data input instead. An intermediate approach is to synthesize a 3D face making use of a single 2D image. In this paper, we present such a method that does not require complicated optimization steps or user-defined parameters, which distinguishes it from existing 3D face reconstruction methods. The method has been used in 2D face recognition experiments to generate a 3D Morphological Model (3DMM) from a single facial training image.
Given a single 2D facial image, a small set of morphological feature points are selected. Their corresponding three-dimensional indices in the morphological model are obtained using the so-called Fast Marching Method. Shape alignment between the 2D input image and the 3D morphological model is achieved using Newton's Method to solve a single nonlinear equation to obtain a scalar parameter. This yields all of the shape parameters of the 3DMM. Texture recovery for the model involves bridging the 2D image representation of the 3DMM using the so-called UV space as an intermediary. Thus the advantage of the proposed method over others in the literature is that a computationally complex geometric problem is simplified and transformed to a 2D-to-3D warping problem. This enhances both the efficiency and accuracy of texture recovery.
We also introduce a quantitative shape measure based on the Bending Invariant Canonical Form to determine the quality of the 3D reconstruction. This is shown to produce a reconstruction error of 5% over the database of 100 stored 3D faces. In addition, the reconstructed 3D faces at various head poses have been used to create 2D images of faces that form the training set for face recognition experiments reported elsewhere.
