Deformable Generic Elastic Models from a single 2D image for facial expression and large pose face together synthesis and recognition

In this paper, we propose an efficient method to reconstruct the 3D models of a human face from a single 2D face image robustness under a variety facial expressions using the Deformable Generic Elastic Model (D-GEM). We extended the Generic Elastic Model (GEM) approach and combined it with statistical information of the human face and deformed generic depth models by computing the distance around face lips. Particularly, we demonstrate that D-GEM can approximate the 3D shape of the input face image more accurately, achieving a better and higher quality of 3D face modeling and reconstruction robustness under a variety of facial expressions compared to the original GEM and Gender and Ethnicity-GEM (GE-GEM) approach. It has been tested on an available 2D face database and new synthesized facial expression and large pose changes together from gallery images. We acquire promising results for handling pose and expression changes based on the proposed method compared to the GEM and GE-GEM.