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

Unsupervised Facial Action Unit Intensity Estimation via Differentiable Optimization