  Recognizing facial expressions from static images or video sequences is a
widely studied but still challenging problem. The recent progresses obtained by
deep neural architectures, or by ensembles of heterogeneous models, have shown
that integrating multiple input representations leads to state-of-the-art
results. In particular, the appearance and the shape of the input face, or the
representations of some face parts, are commonly used to boost the quality of
the recognizer. This paper investigates the application of Convolutional Neural
Networks (CNNs) with the aim of building a versatile recognizer of expressions
in static images that can be further applied to video sequences. We first study
the importance of different face parts in the recognition task, focussing on
appearance and shape-related features. Then we cast the learning problem in the
Semi-Supervised setting, exploiting video data, where only a few frames are
supervised. The unsupervised portion of the training data is used to enforce
three types of coherence, namely temporal coherence, coherence among the
predictions on the face parts and coherence between appearance and shape-based
representation. Our experimental analysis shows that coherence constraints can
improve the quality of the expression recognizer, thus offering a suitable
basis to profitably exploit unsupervised video sequences. Finally we present
some examples with occlusions where the shape-based predictor performs better
than the appearance one.
