Efficient 3D Face Reconstruction from a Single 2D Image by Combining Statistical and Geometrical Information

Abstract

In this paper, we present an efficient algorithm for reconstructing 3D head model from a single 2D image based on using a 3D eigenhead model. This system is composed of two components, offline training of the eigenhead model and online reconstruction of a 3D head model. For the first part, we propose a new 3D head alignment algorithm based on an iterative coarse-to-fine scheme to establish dense point correspondences between 3D head model in the cylindrical coordinate to align the 3D head models in the training data set. In addition, we apply the radial basis function technique to establish dense correspondences between each 3D face model and a reference face model, followed by the principal component analysis technique to compute the statistical eigenhead model. For the 3D face reconstruction from a single image, the proposed algorithm finds the best linear combination of the eigenhead bases that minimizes an energy function composed of distances between the corresponding facial feature points and a one-way partial Haussdorf distance between the facial contours in the image domain. This energy minimization is accomplished by the iterative Levenberg-Marquardt algorithm with the initial guess determined by solving a linear system derived from the image projection constraints for the corresponding facial feature points. Experimental results show that the proposed 3D face reconstruction algorithm provides satisfactory results and takes less than 10 seconds on a regular PC.