We propose a new method to automatically refine a facial disparity map obtained with standard cameras and under conventional
illumination conditions by using a smart combination of traditional computer vision and 3D graphics techniques.
Our system inputs two stereo images acquired with standard (calibrated) cameras and uses dense disparity estimation
strategies to obtain a coarse initial disparity map, and SIFT to detect and match several feature points in the subject's face.
We then use these points as anchors to modify the disparity in the facial area by building a Delaunay triangulation of their
convex hull and interpolating their disparity values inside each triangle. We thus obtain a refined disparity map providing
a much more accurate representation of the the subject's facial features. This refined facial disparity map may be easily
transformed, through the camera calibration parameters, into a depth map to be used, also automatically, to improve the
facial mesh of a 3D avatar to match the subject's real human features.