  The automatic intensity estimation of facial action units (AUs) from a single
image plays a vital role in facial analysis systems. One big challenge for
data-driven AU intensity estimation is the lack of sufficient AU label data.
Due to the fact that AU annotation requires strong domain expertise, it is
expensive to construct an extensive database to learn deep models. The limited
number of labeled AUs as well as identity differences and pose variations
further increases the estimation difficulties. Considering all these
difficulties, we propose an unsupervised framework GE-Net for facial AU
intensity estimation from a single image, without requiring any annotated AU
data. Our framework performs differentiable optimization, which iteratively
updates the facial parameters (i.e., head pose, AU parameters and identity
parameters) to match the input image. GE-Net consists of two modules: a
generator and a feature extractor. The generator learns to "render" a face
image from a set of facial parameters in a differentiable way, and the feature
extractor extracts deep features for measuring the similarity of the rendered
image and input real image. After the two modules are trained and fixed, the
framework searches optimal facial parameters by minimizing the differences of
the extracted features between the rendered image and the input image.
Experimental results demonstrate that our method can achieve state-of-the-art
results compared with existing methods.
