Abstract
We present an automatic disparity-based approach for 3D face modeling, from two frontal and one profile view stereo images, for 3D face recognition applications. Once the images are captured, the algorithm starts by extracting selected 2D facial features from one of the frontal views and computes a dense disparity map from the two frontal images. We then align a low resolution 2D mesh model to the selected features, adjust some of its vertices along the profile line using the profile view, increase its triangular vertices to a higher resolution, and re-project them back on the frontal image. Using the coordinates of the re-projected vertices and their corresponding disparities, we capture and compute the 3D facial shape variations using stereo vision. The final result is a deformed 3D model specific to a given subject's face. Application of the model in 3D face recognition validates the algorithm and shows a promising 98 % recognition rate.
