Regressing Robust and Discriminative 3D Morphable Models
with a very Deep Neural Network
Abstract
The 3D shapes of faces are well known to be discriminative. Yet despite this, they are rarely used for face recognition and always under controlled viewing conditions. We
claim that this is a symptom of a serious but often overlooked problem with existing methods for single view 3D
face reconstruction: when applied “in the wild”, their
3D estimates are either unstable and change for different
photos of the same subject or they are over-regularized
and generic. In response, we describe a robust method
for regressing discriminative 3D morphable face models
(3DMM). We use a convolutional neural network (CNN) to
regress 3DMM shape and texture parameters directly from
an input photo. We overcome the shortage of training data
required for this purpose by offering a method for generating huge numbers of labeled examples. The 3D estimates
produced by our CNN surpass state of the art accuracy on
the MICC data set. Coupled with a 3D-3D face matching
pipeline, we show the first competitive face recognition results on the LFW, YTF and IJB-A benchmarks using 3D face
shapes as representations, rather than the opaque deep feature vectors used by other modern systems.