3D Face Reconstruction from Stereo Images

In this paper we propose a system for 3D face reconstruction from 2D stereo images. Two different stereo matching algorithms are presented; the first is a local method that finds the correspondences of 15 most characteristic facial fiducial points, whose 3D reconstruction allows us to deform a parameterized sparse model. The second method employs a snake grid algorithm which minimizes a multi-resolution energy function; it defines a dense disparity map and determines a dense 3D face model. Besides we investigate how 3D features affect the recognition performance of a face recognition system working on 2D images.