  3D face reconstruction from a single 2D image is a very important topic in
computer vision. However, the current reconstruction methods are usually
non-sensitive to face identities and over-sensitive to facial poses, which may
result in similar 3D geometries for faces of different identities, or obtain
different shapes for the same identity with different poses. When such methods
are applied practically, their 3D estimates are either changeable for different
photos of the same subject or over-regularized and generic to distinguish face
identities. In this paper, we propose a robust solution to solve this problem
by carefully designing a novel Siamese Convolutional Neural Network (SCNN).
Specifically, regarding the 3D Morphable face Model (3DMM) parameters of the
same individual as the same class, we employ the contrastive loss to enlarge
the inter-class distance and meanwhile reduce the intra-class distance for the
output 3DMM parameters. We also propose an identity loss to preserve the
identity information for the same individual in the feature space. Training
with these two losses, our SCNN could learn representations that are more
discriminative for face identity and generalizable for pose variants.
Experiments on the challenging database 300W-LP and AFLW2000-3D have shown the
effectiveness of our method by comparing with state-of-the-arts.
