SL2E-AFRE : Personalized 3D face reconstruction using autoencoder with simultaneous subspace learning and landmark estimation

Abstract

3D face reconstruction from single face image has received much attention in the past decade, as it has been used widely in many applications in the field of computer vision. Despite more accurate solutions by 3D scanners and several commercial systems, they have drawbacks such as the need for manual initialization, time and economy constraints. In this paper, a novel framework for 3D face reconstruction is presented. Firstly, landmarks are localized on the database faces with the proposed landmark-mapping strategy employing a model template. Then, an autoencoder assisted by the proposed energy function to simultaneously learn the facial patch subspace and the keypoints positions is employed to predict the landmarks. Finally, an unique 3D reconstruction is obtained with the proposed predicted landmark based deformation. Meta-parameters are incorporated into the energy function during the training phase to enhance the performance of the autoencoder network in reconstructing the face model. The experiments are carried out on two databases namely the USF Human ID 3-D Database and the Bosphorus 3D face database. The experimental results show that the Autoencoder based Face REconstruction with Simultaneous patch Learning and Landmark Estimation method (SL2E-AFRE) is efficient and the performance of the same is significantly upgraded in each iteration.