  This paper proposes an encoder-decoder network to disentangle shape features
during 3D face reconstruction from single 2D images, such that the tasks of
reconstructing accurate 3D face shapes and learning discriminative shape
features for face recognition can be accomplished simultaneously. Unlike
existing 3D face reconstruction methods, our proposed method directly regresses
dense 3D face shapes from single 2D images, and tackles identity and residual
(i.e., non-identity) components in 3D face shapes explicitly and separately
based on a composite 3D face shape model with latent representations. We devise
a training process for the proposed network with a joint loss measuring both
face identification error and 3D face shape reconstruction error. To construct
training data we develop a method for fitting 3D morphable model (3DMM) to
multiple 2D images of a subject. Comprehensive experiments have been done on
MICC, BU3DFE, LFW and YTF databases. The results show that our method expands
the capacity of 3DMM for capturing discriminative shape features and facial
detail, and thus outperforms existing methods both in 3D face reconstruction
accuracy and in face recognition accuracy.
