  Facial expressions play a significant role in human communication and
behavior. Psychologists have long studied the relationship between facial
expressions and emotions. Paul Ekman et al., devised the Facial Action Coding
System (FACS) to taxonomize human facial expressions and model their behavior.
The ability to recognize facial expressions automatically, enables novel
applications in fields like human-computer interaction, social gaming, and
psychological research. There has been a tremendously active research in this
field, with several recent papers utilizing convolutional neural networks (CNN)
for feature extraction and inference. In this paper, we employ CNN
understanding methods to study the relation between the features these
computational networks are using, the FACS and Action Units (AU). We verify our
findings on the Extended Cohn-Kanade (CK+), NovaEmotions and FER2013 datasets.
We apply these models to various tasks and tests using transfer learning,
including cross-dataset validation and cross-task performance. Finally, we
exploit the nature of the FER based CNN models for the detection of
micro-expressions and achieve state-of-the-art accuracy using a simple
long-short-term-memory (LSTM) recurrent neural network (RNN).
