  In this paper, we present a deep learning based image feature extraction
method designed specifically for face images. To train the feature extraction
model, we construct a large scale photo-realistic face image dataset with
ground-truth correspondence between multi-view face images, which are
synthesized from real photographs via an inverse rendering procedure. The deep
face feature (DFF) is trained using correspondence between face images rendered
from different views. Using the trained DFF model, we can extract a feature
vector for each pixel of a face image, which distinguishes different facial
regions and is shown to be more effective than general-purpose feature
descriptors for face-related tasks such as matching and alignment. Based on the
DFF, we develop a robust face alignment method, which iteratively updates
landmarks, pose and 3D shape. Extensive experiments demonstrate that our method
can achieve state-of-the-art results for face alignment under highly
unconstrained face images.
