Disentangling Features in 3D Face Shapes
for Joint Face Reconstruction and Recognition?
Abstract
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