Disparity-based 3D face modeling using 3D deformable facial mask for 3D face recognition

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. Using the extracted 2D features plus their corresponding disparities in the disparity map, we compute their 3D coordinates. We next align a low resolution 3D mesh model to the 3D features, re-project it’s vertices on the frontal 2D image and adjust its profile line vertices using the profile view. We increase the resolutions of the resulting 2D model only at its center region to obtain a facial mask model covering distinctive features of the face. The computation of the 2D vertices coordinates with their disparities results in a deformed 3D model mask specific to a give subject’s face. Application of the model in 3D face recognition validates the algorithm and shows a high recognition rate. 1

State of the Art in Face Recognition

Notwithstanding the tremendous effort to solve the face recognition problem, it is not possible yet to design a face recognition system with a potential close to human performance. New computer vision and pattern recognition approaches need to be investigated. Even new knowledge and perspectives from different fields like, psychology and neuroscience must be incorporated into the current field of face recognition to design a robust face recognition system. Indeed, many more efforts are required to end up with a human like face recognition system. This book tries to make an effort to reduce the gap between the previous face recognition research state and the future state