  Affect recognition based on subjects' facial expressions has been a topic of
major research in the attempt to generate machines that can understand the way
subjects feel, act and react. In the past, due to the unavailability of large
amounts of data captured in real-life situations, research has mainly focused
on controlled environments. However, recently, social media and platforms have
been widely used. Moreover, deep learning has emerged as a means to solve
visual analysis and recognition problems. This paper exploits these advances
and presents significant contributions for affect analysis and recognition
in-the-wild. Affect analysis and recognition can be seen as a dual knowledge
generation problem, involving: i) creation of new, large and rich in-the-wild
databases and ii) design and training of novel deep neural architectures that
are able to analyse affect over these databases and to successfully generalise
their performance on other datasets. The paper focuses on large in-the-wild
databases, i.e., Aff-Wild and Aff-Wild2 and presents the design of two classes
of deep neural networks trained with these databases. The first class refers to
uni-task affect recognition, focusing on prediction of the valence and arousal
dimensional variables. The second class refers to estimation of all main
behavior tasks, i.e. valence-arousal prediction; categorical emotion
classification in seven basic facial expressions; facial Action Unit detection.
A novel multi-task and holistic framework is presented which is able to jointly
learn and effectively generalize and perform affect recognition over all
existing in-the-wild databases. Large experimental studies illustrate the
achieved performance improvement over the existing state-of-the-art in affect
recognition.
