Human facial expression interpretation has been a classic field of study in psychology, and it has benefited from seminal contributions by renowned researchers such as P. Ekman, who characterized and studied the manifestation of prototypical emotions through changes in facial features. From the computer vision perspective, solving the problem of automated facial expression interpretation is a cornerstone towards high-level human computer interaction, and its study has become an active topic of research in the last decades.

In order to study facial expressions in a systematic way, Ekman and his collaborators designed the Facial Action Coding System (FACS). FACS relies on identifying visible local appearance variations in the human face, called Action Units (AUs), produced by to contraction or relaxation in any of its 30 muscles (e.g., a raised eyebrow). AUs constitute therefore a natural physiological basis for face analysis, in which any facial expression can be, potentially, represented by their combinations.

The existence of a physiological basis for a computer vision domain is a rare luxury, as it allows focusing on the essential atoms of the problem and, by virtue of their multiple possible combinations, opens the door to a wide range of applications beyond the emotion classification domain such as psychological, medical, legal, entertainment, etc.