  Bringing empathy to a computerized system could significantly improve the
quality of human-computer communications, as soon as machines would be able to
understand customer intentions and better serve their needs. According to
different studies (Literature Review), visual information is one of the most
important channels of human interaction and contains significant behavioral
signals, that may be captured from facial expressions. Therefore, it is
consistent and natural that the research in the field of Facial Expression
Recognition (FER) has acquired increased interest over the past decade due to
having diverse application area including health-care, sociology, psychology,
driver-safety, virtual reality, cognitive sciences, security, entertainment,
marketing, etc. We propose a new architecture for the task of FER and examine
the impact of domain discrimination loss regularization on the learning
process. With regard to observations, including both classical training
conditions and unsupervised domain adaptation scenarios, important aspects of
the considered domain adaptation approach integration are traced. The results
may serve as a foundation for further research in the field.
