Learning 3D faces from 2D images via Stacked Contractive Autoencoder

3D face reconstruction from a 2D face image has been found important to various applications such as face detection and recognition because a 3D face provides more semantic information than 2D image. This paper proposes a deep learning framework for 3D face reconstruction. The framework is designed to compute subspace feature of arbitrary face image, then map the feature to its counterpart in another subspace learned with 3D faces, and reconstruct the 3D face using the counterpart feature. During the course of training, we learn 2D and 3D subspaces through Stacked Contractive Autoencoders (SCAE), use a one-layer fully connected neural network to learn the mapping, and use the pre-trained parameters of the SCAEs and the one-layer network to initialize a deep feedforward neural network whose input are face images and output are 3D faces. The network is optimized by gradient descent algorithm with back-propagation. Extensive experimental results on various data sets indicate the effectiveness of the proposed SCAE-based 3D face reconstruction method.