Abstract
We present a multimodal approach for 3D face modeling and recognition from two frontal and one profile view stereo images of the face. 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 mesh model to the selected features, adjust its vertices at the selected features and along the profile line using the profile view, increase its 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 triangulation. 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 with a high recognition rate.