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Human face exhibits an inherent hierarchy in its representations (i.e.,holistic facial expressions can be encoded via a set of facial action units(AUs) and their intensity). Variational (deep) auto-encoders (VAE) have showngreat results in unsupervised extraction of hierarchical latent representationsfrom large amounts of image data, while being robust to noise and otherundesired artifacts. Potentially, this makes VAEs a suitable approach forlearning facial features for AU intensity estimation. Yet, most existingVAE-based methods apply classifiers learned separately from the encodedfeatures. By contrast, the non-parametric (probabilistic) approaches, such asGaussian Processes (GPs), typically outperform their parametric counterparts,but cannot deal easily with large amounts of data. To this end, we propose anovel VAE semi-parametric modeling framework, named DeepCoder, which combinesthe modeling power of parametric (convolutional) and nonparametric (ordinalGPs) VAEs, for joint learning of (1) latent representations at multiple levelsin a task hierarchy1, and (2) classification of multiple ordinal outputs. Weshow on benchmark datasets for AU intensity estimation that the proposedDeepCoder outperforms the state-of-the-art approaches, and related VAEs anddeep learning models.

DeepCoder: Semi-parametric Variational Autoencoders for Automatic Facial Action Coding