  Automatic understanding of human affect using visual signals is a problem
that has attracted significant interest over the past 20 years. However, human
emotional states are quite complex. To appraise such states displayed in
real-world settings, we need expressive emotional descriptors that are capable
of capturing and describing this complexity. The circumplex model of affect,
which is described in terms of valence (i.e., how positive or negative is an
emotion) and arousal (i.e., power of the activation of the emotion), can be
used for this purpose. Recent progress in the emotion recognition domain has
been achieved through the development of deep neural architectures and the
availability of very large training databases. To this end, Aff-Wild has been
the first large-scale "in-the-wild" database, containing around 1,200,000
frames. In this paper, we build upon this database, extending it with 260 more
subjects and 1,413,000 new video frames. We call the union of Aff-Wild with the
additional data, Aff-Wild2. The videos are downloaded from Youtube and have
large variations in pose, age, illumination conditions, ethnicity and
profession. Both database-specific as well as cross-database experiments are
performed in this paper, by utilizing the Aff-Wild2, along with the RECOLA
database. The developed deep neural architectures are based on the joint
training of state-of-the-art convolutional and recurrent neural networks with
attention mechanism; thus exploiting both the invariant properties of
convolutional features, while modeling temporal dynamics that arise in human
behaviour via the recurrent layers. The obtained results show premise for
utilization of the extended Aff-Wild, as well as of the developed deep neural
architectures for visual analysis of human behaviour in terms of continuous
emotion dimensions.
